DETAILED ACTION
Remarks
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation “based at least on the stress data and the trained AI and/or ML model, at least one of (i) identify at least one occurrence of at least one potential for at least one human error or (ii) predict the at least one occurrence of the at least one potential for the at least one human error”. Similarly, claim 20 recites the limitation “based at least on the stress data and the trained Al and/or ML model, at least one of (i) identifying, by the at least one processor, at least one occurrence of at least one potential for at least one human error or (ii) predicting, by the at least one processor, the at least one occurrence of the at least one potential for the at least one human error”. This is interpreted as a computer-implemented functional claim, and thus is analyzed according to MPEP 2161.01(I) to determine whether there is adequate written description for the computer-implemented function claim limitation of claims 1 and 20. Additionally, these limitations define a desired result, e.g., identification or prediction of error. Although the specification discloses a computer, it does not disclose the algorithm (e.g., the necessary steps and/or flowcharts) that performs this computer-implemented function, i.e., how steps (i) or (ii) are achieved based on the stress data and the trained AI or ML model. An algorithm is defined, for example, as “a finite sequence of steps for solving a logical or mathematical problem or performing a task.” Microsoft Computer Dictionary (5th ed., 2002). Applicant may “express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure.” Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding “whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved”). In this application, the specification does not provide a disclosure of the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, but instead merely restates the function recited in the claims. Therefore, the written description requirement is not satisfied.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
STEP 1 = YES: The claimed invention is to a product (claims 1-19) and a process (claim 20), and thus fall under one of the four statutory categories (Step 1: YES).
STEP 2A, Prong 1 = YES: The claim(s) recite(s) a series of steps which can be practically performed by one or more humans through mental process (i.e., observation, evaluation, judgement, and/or opinion)(see MPEP § 2106.04(a)(2), subsection III) and/or certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II). Moreover, the claims recite steps akin to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, which the court in Electric Power Group held to recite a mental process. Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016).
This includes a system comprising:
obtain stress data, the stress data including objective measures of stresses, the stresses including at least one physical stress, at least one external stress, and at least one mental stress;
obtain a … model; based at least on the stress data … at least one of (i) identify at least one occurrence of at least one potential for at least one human error or (ii) predict the at least one occurrence of the at least one potential for the at least one human error; and upon an identification and/or a prediction of the at least one occurrence of the at least one potential for the at least one human error, output at least one instruction to cause at least one of … (b) at least one modification to an amount of … assistance provided to the at least one user, (c) at least one modification of content presented to the at least one user, (d) at least one alert to the at least one user, or (e) a presentation of at least one solution to increase comprehension by the at least one user (i.e., mental observation to collect data (stress data) and evaluation of the collected data (identifying, predicting, modifying; certain methods of human activity, including interactions between individuals, e.g., teaching).
The following additional limitations merely further limit the judicial exceptions identified above, and thus recite an abstract idea:
wherein the …model is a causal…model;
wherein the causal … model is based at least on a human factors causal loop analysis;
wherein the at least one user comprises at least one operator of at least one vehicle;
wherein the at least one vehicle comprises at least one of at least one aircraft or at least one spacecraft;
wherein the at least one vehicle comprises the at least one aircraft,
wherein the at least one aircraft comprises at least one manned aircraft;
wherein the at least one vehicle comprises the at least one aircraft,
wherein the at least one aircraft comprises at least one unmanned aerial vehicle (UAV);
wherein the at least one UAV comprises multiple UAVs,
wherein at least two of the multiple UAVs are operated by a single user of the at least one user;
wherein the at least one physical stress comprises at least one measurement of at least one of: at least one nutrition factor, at least one work factor, at least one exercise factor, at least one sleep factor, at least one hydration factor, or at least one stimulant factor;
wherein the at least one physical stress comprises the measurements of the at least one nutrition factor, the at least one work factor, the at least one exercise factor, the at least one sleep factor, the at least one hydration factor, and the at least one stimulant factor;
wherein the at least one external stress comprises at least one measurement of at least one of: at least one execution factor, at least one machine status factor, or at least one world factor;
wherein the at least one external stress comprises the measurements of the at least one execution factor, the at least one machine status factor, and the at least one world factor;
wherein the at least one mental stress comprises at least one measurement of at least one of: at least one situation awareness factor, at least one cognitive workload factor, or at least one training factor;
wherein the at least one mental stress comprises the measurements of the at least one situation awareness factor, the at least one cognitive workload factor, and the at least one training factor;
wherein the stress data includes subjective measures of the stresses and the objective measures of the stresses,
wherein some of the stress data is collected in real-time and other of the stress data is collected prior to in real-time,
wherein some of the stresses are directly measurable,
wherein other of the stresses are not directly measurable;
wherein the … model uses weightings, the weightings comprising a first weighting of at least 20% for the at least one physical stress, a second weighting of at least 20% for the at least one external stress, and a third weighting of at least 20% for the at least one mental stress,
wherein the weightings total 100%;
wherein the at least one physical stress is multiple physical stresses comprising at least one nutrition factor, at least one work factor, at least one exercise factor, at least one sleep factor, and at least one stimulant factor,
wherein the first weighting for the multiple physical stresses comprises physical stress sub- weightings of at least 10% for each of the at least one nutrition factor, the at least one work factor, the at least one exercise factor, the at least one sleep factor, and the at least one stimulant factor, wherein the physical stress sub-weightings total 100%;
wherein the at least one mental stress is multiple mental stresses comprising at least one situation awareness factor, at least one cognitive workload factor, and at least one training factor, wherein the second weighting for the multiple mental stresses comprises mental stress sub-weightings of at least 15% for each of the at least one situation awareness factor, the at least one cognitive workload factor, and the at least one training factor,
wherein the mental stress sub-weightings total 100%;
wherein the at least one physical stress is multiple physical stresses comprising at least one nutrition factor, at least one work factor, at least one exercise factor, at least one sleep factor, and at least one stimulant factor,
wherein the first weighting for the multiple physical stresses comprises physical stress sub-weightings of at least 10% for each of the at least one nutrition factor, the at least one work factor, the at least one exercise factor, the at least one sleep factor, and the at least one stimulant factor, wherein the physical stress sub-weightings total 100%,
wherein the at least one mental stress is multiple mental stresses comprising at least one situation awareness factor, at least one cognitive workload factor, and at least one training factor,
wherein the second weighting for the multiple mental stresses comprises mental stress sub-weightings of at least 15% for each of the at least one situation awareness factor, the at least one cognitive workload factor, and the at least one training factor,
wherein the mental stress sub-weightings total 100%,
wherein the at least one exercise factor comprises multiple exercise factors comprising whether each of the at least one user exercised within eight hours before a work shift of said user and an amount of exercise each of the at least one user gets on average,
wherein each of the multiple exercise factors has an exercise factor sub-weighting of at least 25%,
wherein the exercise factor sub-weightings total 100%,
wherein the at least one training factor comprises multiple training factors comprising a total number hours of aircraft training for each of the at least one user, a number of combat missions flown for each of the at least one user, whether each of the at least one user has a flown a multi-crew aircraft, a number of years of service in an industry for each of the at least one user, a current service role for each of the at least one user, and a number of years in aircraft-related roles for each of the at least one user,
wherein each of the multiple experience factors has an experience factor sub-weighting of at least 10%,
wherein the experience factor sub-weightings total 100%.
Additionally, this includes a method comprising:
obtaining … stress data, the stress data including objective measures of stresses, the stresses including at least one physical stress, at least one external stress, and at least one mental stress; based at least on the stress data … at least one of (i) identifying … at least one occurrence of at least one potential for at least one human error or (ii) predicting … the at least one occurrence of the at least one potential for the at least one human error; and upon an identification and/or a prediction of the at least one occurrence of the at least one potential for the at least one human error, outputting … at least one instruction to cause at least one of … (b) at least one modification to an amount of … assistance provided to the at least one user, (c) at least one modification of content presented to the at least one user, (d) at least one alert to the at least one user, or (e) a presentation of at least one solution to increase comprehension by the at least one user (i.e., mental observation to collect data (stress data) and evaluation of the collected data (identifying, predicting, modifying; certain methods of human activity, including interactions between individuals, e.g., teaching).
The steps identified above are akin to organizing human activity, mental processes, and/or mathematical concepts, and thus fall within an enumerated category of abstract ideas. Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited steps above, the use of such physical aid does not negate the mental nature of these limitations. Therefore, the claims recite an abstract idea (Step 2A, Prong 1: YES).
STEP 2A, Prong 2 = NO: This judicial exception is not integrated into a practical application.
To the extent the claims recite additional elements related to defining a computer environment to implement the abstract idea above (i.e., at least one processor communicatively coupled to the at least one sensor, wherein the at least one processor is configured to: perform the steps under Prong 1; defining the model as a trained artificial intelligence (AI) and/or machine learning (ML) model, wherein the trained artificial intelligence (AI) and/or machine learning (ML) model is a causal Al model; and wherein the causal Al model is based at least on a human factors causal loop analysis, and performing certain steps based on the trained Al and/or ML model), they are recited at a high level of generality, with no details defining a particular machine, such that they do not amount to a technological improvement. Rather, each step does no more than require a generic processor to perform generic computer functions. Moreover, the steps of the claims do not improve the functioning of the computer itself, nor do the claims delineate steps through which the machine learning technology achieves an improvement. The specification does not identify improvements as to how the computer or AI/ML itself operates. Rather, the claims merely apply generic computers, generic AI/ML models, and generic sensors to a field of use which is identified as the abstract idea under Prong 1. Therefore, the inclusion of these additional elements in the claims do not integrate the judicial exception into a practical application.
To the extent the claims recite additional elements related to a physical component for providing data collection (i.e., obtain sensor data from one or more of the at least one sensor; and obtaining, by at least one processor, sensor data from one or more of the at least one sensor … at least some of the objective measures of stresses associated with the sensor data), the claims do not recite a particular configuration of the sensors or a particular method of using the raw data from the sensors in order to achieve a technical improvement. Rather, the claimed sensors are merely referred to by name alone, e.g., sensors, to perform insignificant pre-solution data gathering activity, which but for the generic recitation of sensors, is practically capable of being performed by human analog, e.g., by mental observation. Therefore, the use of sensors in the claims does not integrate the judicial exception into a practical application.
To the extent the claims recite additional elements related to a physical component for providing data output (i.e., output at least one instruction to cause at least one of (a) at least one modification to at least one human machine interface (HMI) device that interfaces with at least one user and generally describing the output as automated digital assistance), the claims do not recite a particular manner of how the modifications are performed, but rather recite the modifications at a high level of generality. Thus, the claimed additional elements defining output as a modification to an HMI or in the context of a digital environment amount to insignificant post-solution data output activity. Therefore, these limitations do not integrate the judicial exception into a practical application.
It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the physical components identified above does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. Therefore, the claims are directed to an abstract idea, not an improvement in computer functionality or to any other technology or technical field (Step 2A, Prong 2: YES).
STEP 2B = NO: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as provided under Prong 2, the additional elements are recited at a high level of generality, and for the purpose of insignificant pre and post-solution activity. Moreover, the specification of the instant application further demonstrates that the additional elements are recited for their well-understood, routine and conventional functionality, which refers to elements of the computer system in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)(e.g., see par. 0009: well- known features may not be described in detail to avoid unnecessarily complicating the instant disclosure; par. 0021: Each computing device 104 may be any suitable computing device; par. 0022: Each sensor 110 may be any suitable sensor; par. 0028: Each HMI 126 may be any suitable HMI; par. 0033: referring to trained artificial intelligence, machine learning, and causal AI models by name alone without any disclosed technical details or any disclosed technological improvement; par. 0040: Some embodiments may include any suitable system 100 setup). Thus, the use of these additional elements do not amount to a technical solution to a technological problem, and thus are interpreted as merely automating a manual process, which the courts have held to be insufficient in showing an improvement in computer-functionality. See Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017); see also LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential).
Therefore, the claims are not directed to significantly more than the abstract idea (Step 2B: NO).
Therefore, claims 1-20 are not directed to patent eligible subject matter.
Conclusion
The attached FORM 892 includes citations to prior art deemed relevant to the disclosed invention, but not relied upon in the rejection above, including:
US 2020/0057487 A1 to SICCONI (par. 0095: Embodiments described herein may enable safer driving by providing real time feedback to the driver about potentially hazardous conditions to prevent accidents caused by inattentiveness or impaired health conditions).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Hull whose telephone number is 571-272-0996. The examiner can normally be reached on Monday-Friday from 8:00am to 5:00pm MST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached at telephone number 571-272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES B HULL/Primary Examiner, Art Unit 3715