DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/523,500 filed on 11/29/2023. Claims 1-2 and 5-6 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/523,500 filed 11/29/2023 claims foreign priority to 202211569304.0, filed 12/08/2022.
Response to Amendment
4. In the response filed December 04, 2025, Applicant amended claim 1, and canceled claims 3-4 and 7-10. No new claims were presented for examination.
5. Applicant cancelled claims 7-10. Accordingly, the Claim Interpretation under 35 U.S.C. 112(f) has been removed.
6. Applicant cancelled claims 7-10. Accordingly, the previously issued claim rejection under 35 U.S.C. 112(a) has been withdrawn.
7. Applicant cancelled claims 7-10. Accordingly, the previously issued claim rejection under 35 U.S.C. 112(b) has been withdrawn.
8. Applicant's amendments to claim 1 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
9. Applicant's arguments filed December 04, 2025, have been fully considered.
10. Applicant submits “With regard to the Examiner's allegation that the claim 1 as reciting "steps for managing scheduling activities" that fall within "certain methods of organizing human activity," as well as "mathematical concepts," and concludes that the claim is directed to an abstract idea. Applicant respectfully submits that the claims are directed to a specific technical improvement in civil aviation flight-string scheduling, and not to a generic method of organizing human activity.” [Applicant’s Remarks, 12/04/2025, page 4]
The Examiner respectfully disagrees. Contrary to Applicant’s assertions, claim 1 is directed to organizing and evaluating human activity, namely assessments of flight duty information and fatigue associated with crew assignments. The recited steps relate to segmenting task information, assigning weights to factors, and determining a fatigue grade merely define rules for organizing and analyzing information about human work performance. The claim does not recite any technological implementation or technical improvement, but instead addresses how such information is evaluated. For the reasons above, this argument is found unpersuasive.
11. Applicant submits “The claimed method and system improve this technical field by enabling the early, objective, and repeatable evaluation of fatigue risk at the flight-string level, which can then be used to design and allocate flight strings in a manner that reduces operational fatigue hazards. Claim 1 including a specific hierarchical index structure (time planning, environmental, workload), weighting scheme, normalization of heterogeneous factors, aggregation into duty-segment fatigue values, and conversion of the overall second fatigue value into a four-level Grade I-IV classification with defined thresholds rather than to the abstract idea of "managing scheduling activities" in the abstract, thus present invention outputs a quantified second fatigue value and a fatigue grade (Grade I-IV) for the flight string.” [Applicant’s Remarks, 12/04/2025, page 5]
The Examiner respectfully disagrees. Although Applicant submits that claim 1 improves the technical field by providing early and objective fatigue evaluation and includes a hierarchical index structure, weighting, normalization, aggregation, and grading, these features merely define analytical rules and mathematical calculations applied to the information. The claimed outputs of a fatigue value and a fatigue grade represent the result of the abstract evaluation process and do not constitute a technical improvement or a practical application. The claim does not specify any technical mechanism or implementation that applies the results to a technological system or effects a technical change. For the reasons above, this argument is found unpersuasive.
12. Applicant submits “that even assuming arguendo that some limitations in claim 1 could be characterized as involving "mathematical concepts" but amended claim 1 integrates the invention into a practical application in a technical field.” [Applicant’s Remarks, 12/04/2025, page 5]
The Examiner respectfully disagrees. In response, the Examiner first points out that as noted in the previous Office Action [10/27/2025], the steps recited in claim 1 are disembodied steps (i.e., the steps are disembodied since no device/hardware is relied on for performing the claimed limitations).
In response to Applicant’s argument that “even assuming arguendo that some limitations in claim 1 could be characterized as involving "mathematical concepts" but amended claim 1 integrates the invention into a practical application in a technical field,” it is noted that Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. (pg. 55 of Fed. Reg. / Vol. 84, No. 4 – published Jan. 7, 2019).
In this instance, there are no additional claim elements besides the judicial exception. As noted in the previous Office Action, dated 10/01/2024, because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. Accordingly, this argument is found unpersuasive.
13. Applicant submits “With regard to the step 2B, the Applicant submits that the claims recites the non-conventional and non-routine combination of elements according to aviation fatigue. The particular combination of hierarchical index structure, weighting, normalization, multi-segment aggregation, and threshold-based grading is not alleged by the Examiner, nor shown in the prior art of record, to be well-understood, routine, and conventional. Rather, it reflects a specific engineering model of how different operational factors combine to produce fatigue risk across multiple duty segments of a flight string, and how that risk should be quantified and expressed as an actionable grade for airline scheduling. This combination of elements yields an improved result an objective, flight- string-level fatigue grade that can be used to design and assign flight strings more safely over the purely subjective or purely regulatory-limit methods discussed in the background section of the present application.” [Applicant’s Remarks, 12/04/2025, page 6]
The Examiner respectfully disagrees. In response, the Examiner first points out that as noted in the previous Office Action [10/27/2025], the steps recited in claim 1 are disembodied steps (i.e., the steps are disembodied since no device/hardware is relied on for performing the claimed limitations).
In response to Applicant’s argument that “even assuming arguendo that some limitations in claim 1 could be characterized as involving "mathematical concepts" but amended claim 1 integrates the invention into a practical application in a technical field,” it is noted that Under Step 2A Prong Two of the eligibility inquiry, any additional elements are evaluated individually and in combination to determine whether they integrate the judicial exception into a practical application, with consideration of the following exemplary considerations that may be indicative of a practical application: an additional element that reflects an improvement to the functioning of a computer or to any other technology or technical field, applying the exception with a particular machine, applying the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, effecting a transformation of a particular article to a different state or thing, and applying or using the judicial exception some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. (pg. 55 of Fed. Reg. / Vol. 84, No. 4 – published Jan. 7, 2019).
In this instance, there are no additional claim elements besides the judicial exception. As noted in the previous Office Action, dated 10/01/2024, because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible. Accordingly, this argument is found unpersuasive.
14. Applicant submits “that the limitation "collecting task information of a flight string, and extracting first factors influencing a flight string fatigue degree based on the task information" of claim 1 is not taught by Rangan.” [Applicant’s Remarks, 12/04/2025, page 6]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “collecting task information of a flight string, and extracting first factors influencing a flight string fatigue degree based on the task information." However, in at least paragraphs 0005, 0006, 0022, 0062, 0066, Rangan teaches obtaining flight information related to the crew’s schedule and other relevant factors, and using this information to determine predicted fatigue profiles for flight crew members. These teachings correspond to the claimed step of collecting task information of a flight string, and extracting first factors influencing a flight string fatigue degree based on the task information. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
15. Applicant submits “that the limitation "constructing an index evaluation system based on the first factors" of claim 1 as amended is not taught or suggested by Rangan. In the present claims and.” [Applicant’s Remarks, 12/04/2025, page 7]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “constructing an index evaluation system based on the first factors." However, in at least paragraphs 0067 and 0101, Rangan teaches the disputed limitation. Paragraphs 0067 and 0101 describe weighting and prioritizing fatigue indicators based on their relevance or correlation to predicted fatigue levels. This corresponds to constructing an evaluation system (i.e., index evaluation system) based on the first factors. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
16. Applicant submits “that the limitation "carrying out a segment processing on the task information to obtain duty period segments" of claim 1 is not taught or suggested by Rangan.” [Applicant’s Remarks, 12/04/2025, page 7]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “carrying out a segment processing on the task information to obtain duty period segments." However, in at least paragraphs 0060, 0065, 0071, 0080, 0086, Rangan teaches the disputed limitation. These paragraphs describe analyzing crew history and flight schedule information over multiple flights and time periods, including evaluating factors such as flight start times, rets periods, and time between flights. These teachings correspond to “carrying out a segment processing on the task information to obtain duty period segment,” as the claim’s segmentation of task information mirrors the division of crew schedules into discrete periods for fatigue evaluation. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
17. Applicant submits “that the limitation "carrying out a quantitative characterization on each of the first factors to obtain factor intensities of the duty period segments" of claim 1 is not taught or suggested by Rangan.” [Applicant’s Remarks, 12/04/2025, page 8]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “carrying out a quantitative characterization on each of the first factors to obtain factor intensities of the duty period segments." However, in at least paragraph 0069, Rangan teaches the disputed limitation. Paragraph 0069 explicitly teaches evaluating predicted fatigue profiles against threshold fatigue levels for specific duty periods, which corresponds to “carrying out a quantitative characterization on each of the first factors to obtain factor intensities of the duty period segments.” In addition, Rangan in paragraphs 0065 and 0074 describes analyzing crew history and fatigue indicator profiles over multiple flights and time periods to quantify the effect of each factor on predicted fatigue. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
18. Applicant submits “that the limitation "calculating first fatigue values of the duty period segments based on weight coefficients and the factor intensities" of claim 1 is not taught or suggested by Rangan.” [Applicant’s Remarks, 12/04/2025, page 8]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “calculating first fatigue values of the duty period segments based on weight coefficients and the factor intensities." However, in at least paragraphs 0022, 0065, 0067, 0074, Rangan teaches the disputed limitation. Rangan, in paragraphs 0022, 0065, 0067, 0074, teaches combining weighted fatigue factors and predicted fatigue indicator values to determine fatigue levels for specific time periods. This corresponds to “calculating first fatigue values of the duty period segments based on weight coefficients and the factor intensities,” as the first fatigue values reflect the weighted contributions of each factor to the overall fatigue for each duty period segment. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
19. Applicant submits “that the limitation "calculating a second fatigue value of the whole flight string based on the first fatigue values" of claim 1 is not taught or suggested by Rangan. ”[Applicant’s Remarks, 12/04/2025, page 8]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Rangan does not teach “calculating a second fatigue value of the whole flight string based on the first fatigue values.” However, Rangan was only asserted as teaching “calculating a second fatigue value of the whole flight string.” In at least paragraphs 0005 and 0022, Rangan teaches “calculating a second fatigue value of the whole flight string” by determining overall fatigue levels for one or more flight crew members by combining predicted fatigue values from individual time periods. This corresponds to “calculating a second fatigue value of the whole flight string,” as the second fatigue value represents the aggregated fatigue across all duty period segments. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Rangan teaches calculating a second fatigue value of the whole flight string. For the reasons above, this argument is found unpersuasive.
20. Applicant submits “that the limitation "determining a flight string fatigue grade based on the second fatigue value by assigning the flight string to one of a plurality of fatigue grades including Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value" of claim 1 is not taught or suggested by Rangan.” [Applicant’s Remarks, 12/04/2025, page 9]
In response to Applicant’s argument that Driscoll does not teach “determining a flight string fatigue grade based on the second fatigue value by assigning the flight string to one of a plurality of fatigue grades including Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value,” the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 12/04/2025, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the new ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and citations to address the amended limitations in claim 1 and supports a conclusion of obviousness of the amended claims.
21. Applicant submits “Konop does not cute the deficiencies of Rangan…There is no disclosure of (i) dividing a "flight string" into multiple duty period segments, (ii) computing a distinct "first fatigue value" for each such duty period segment based on factor intensities and weight coefficients, and then (iii) calculating a "second fatigue value" of the whole flight string based on those first fatigue values as recited in claim 1 and detailed in the specification.” [Applicant’s Remarks, 12/04/2025, page 9]
In response to Applicant’s argument that “Konop does not cure the deficiencies of Rangan…There is no disclosure of (i) dividing a "flight string" into multiple duty period segments, (ii) computing a distinct "first fatigue value" for each such duty period segment based on factor intensities and weight coefficients, and then (iii) calculating a "second fatigue value" of the whole flight string based on those first fatigue values as recited in claim 1 and detailed in the specification,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., dividing a "flight string" into multiple duty period segments) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
22. Applicant submits “Konop does not teach or suggest the claimed hierarchical index evaluation system in which first factors are classified into first-level indexes comprising time planning factors, environmental factors and workload factors, the first factors in each group are taken as second-level indexes, and weight coefficients are calculated for each first factor within this structure.” [Applicant’s Remarks, 12/04/2025, page 10]
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., hierarchical index evaluation system in which first factors are classified into first-level indexes comprising time planning factors, environmental factors and workload factors, the first factors in each group are taken as second-level indexes, and weight coefficients are calculated for each first factor within this structure) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Moreover, it is noted that Konop was not asserted as disclosing the limitations “classifying the first factors to obtain first-level indexes,” and “taking the first factors included in the first-level indexes as second-level indexes.” Accordingly, this argument is deemed moot.
23. Applicant submits “Applicant also respectfully disagrees that it would have been obvious to modify Rangan with Konop.” [Applicant’s Remarks, 12/04/2025, page 10]
In response to Applicant’s argument that there is no motivation to combine the cited references, the Examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, it is noted that the examiner has provided reasoning articulating why it would have been obvious to combine the references as proposed. The Examiner notes that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). The Examiner points out that the rejection of claim 1 provides an articulated line of reasoning based on the teachings of the prior art, the knowledge of one skilled in the art, and the motivation to modify the prior art to arrive at the conclusion of obviousness of claimed invention, which is a permissible means to support the legal conclusion of the obviousness of the claimed subject matter. See KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 418, 82 USPQ2d at 1396 (quoting In re Kahn, 441 F.3d 977, 988, 78 USPQ2d 1329, 1336 (Fed. Cir. 2006)).
Rangan and Konop are directed to analogous fields, as both are related to employee evaluation and fatigue assements in scheduling systems. It would have been obvious to one of ordinary skill in the art to combine Konop’s teaching of calculating first fatigue values for duty segments and aggregating them to a total fatigue value with Rangan’s fatigue prediction framework, because each reference addresses the same problem for managing fatigue risk. The combination uses known techniques in a predictable manner. It is sufficient that the combination reflects a predictable application of known techniques within the relevant field.
As the claims have been given their "broadest reasonable interpretation consistent with the specification", the Examiner asserts that the scope and contents of the prior art have been determined, thereby satisfying the first factual inquiry set forth by Graham v. John Deere Co. The Examiner applied the teachings of Rangan and Konop, and determined the deficiencies, thereby ascertaining the differences between the prior art and the claims at issue. The Examiner has fulfilled the role of factfinder while resolving the Graham inquiries, as per MPEP 2141, and determined that the level of ordinary skill in the art is reflected by the prior art itself, thereby resolving the level of ordinary skill in the pertinent art. The Examiner asserts that the Graham factual inquiries have been properly resolved, resulting in a proper prima facie case of obviousness. Accordingly, this argument is found unpersuasive.
24. Applicant submits “With regard to the rejection of claim 2, Applicant respectfully disagree with the Examiner and submit that the claim 2 does not merely recite generic "risk factors," but specifies that the first factors comprise a structured set for a flight string as an object: duty duration, duty start time, duty end time, take-off and landing time, stop-over duration, flight direction and crossing time zones, airport difficulty, pre-order duty and rest, number of flight segments and staffing. Neither Rangan nor Konop, alone or in combination, disclose or suggest this specific arrangement of factors in the context of a flight-string-level index evaluation system. Rangan's cited passages concern various.” [Applicant’s Remarks, 12/04/2025, page 11]
In response to Applicant’s argument that “With regard to the rejection of claim 2, Applicant respectfully disagree with the Examiner and submit that the claim 2 does not merely recite generic "risk factors," but specifies that the first factors comprise a structured set for a flight string as an object: duty duration, duty start time, duty end time, take-off and landing time, stop-over duration, flight direction and crossing time zones, airport difficulty, pre-order duty and rest, number of flight segments and staffing,” it is noted that Rangan at paragraphs 0007, 0041, 0059-0060, 0068-0069, 00074 discloses a variety of fatigue related factors for flight crew members, including duty duration, duty start and end times, takeoff and landing time, time zone changes, rest periods, and number of flight segments. Known o paragraphs 0080, 0087, 0091 similarly teaches evaluating multiple fatigue factors such as duty times, takeoffs and landings, rest, and staffing levels. The combination of these refences teaches and at least suggest the claimed “wherein the first factors comprise: a duty duration, a duty start time, a duty end time, a take-off and landing time, a stop-over duration, a flight direction and crossing time zones, an airport difficulty, a pre-order duty and rest, a number of flight segments and staffing.” Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that Rangan and Konop teach the disputed limitation. For the reasons above, this argument is found unpersuasive.
25. Applicant submits “even in combination, Rangan and Konop fail to teach or suggest the claimed method for obtaining factor intensities via explicit down limits and normalization processing as recited in claim 5.” [Applicant’s Remarks, 12/04/2025, page 11]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 5, Applicant argues that “even in combination, Rangan and Konop fail to teach or suggest the claimed method for obtaining factor intensities via explicit down limits and normalization processing as recited in claim 5." Rangan, in paragraphs 0067, 0069, 0101, teaches weighting and prioritizing fatigue indicators based on relevance and correlation to predicted fatigue, and establishing dynamic thresholds for duty periods. Konop in paragraph 0024 explicitly teaches assigning numeric values to fatigue factors according to relative impact and establishing upper and lower list for factor accumulations. The combination of Rangan and Konop teaches and at least suggest performing normalization on quantified factors to obtain factor intensities as recited in claim 5. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the Rangan and Konop combination teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
26. Applicant submits “Everman does not disclose defining per-factor upper and lower influence limits and nor using such normalized factor intensities in a hierarchical index system for flight-string fatigue. The Examiner also does not identify any teaching in Everman of the particular form recited in claim 6. Accordingly, even in combination with Rangan and Konop, Everman does not teach or suggest the claimed calculation method of the normalization processing of claim 6.” [Applicant’s Remarks, 12/04/2025, page 12]
In response, the Examiner agrees. Accordingly, the previously issued claim rejection of claim 6 under 35 U.S.C. 103 has been withdrawn.
27. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action
Claim Rejections - 35 USC § 101
28. 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.
29. Claims 1-2 and 5-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
30. Claims 1-2 and 5-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-2, 5-6) is directed to at least one potentially eligible category of subject matter (i.e., process). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-2 and 5-6 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in the MPEP 2106 because the claims recite steps for managing scheduling activities, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), and steps that that fall into the “Mathematical Concepts,” such as mathematical relationships, formulas and calculations, abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: collecting task information of a flight string, and extracting first factors influencing a flight string fatigue degree based on the task information; constructing an index evaluation system based on the first factors, wherein constructing the index evaluation system comprises: classifying the first factors to obtain first-level indexes comprising time planning factors, environmental factors and workload factors, and taking the first factors included in the first-level indexes as second-level indexes, and calculating a weight coefficient of each of the first factors; carrying out a segment processing on the task information to obtain duty period segments; carrying out a quantitative characterization on the each of the first factors to obtain factor intensities of the duty period segments; calculating first fatigue values of the duty period segments based on weight coefficients and the factor intensities; and calculating a second fatigue value of the whole flight string based on the first fatigue values; and determining a flight string fatigue grade based on the second fatigue value by assigning the flight string fatigue grade to one of a plurality of fatigue grades consisting of Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value. These steps describe managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and are part of the abstract idea falling under “Certain Methods of Organizing Human Activity” and steps that can be performed in the human mind, and therefore fall under the “Mathematical Concepts” abstract idea grouping.
Because the above-noted limitations recite steps falling within the “Certain methods of organizing human activity” abstract idea grouping and the “Mathematical Concepts” abstract idea grouping, they have been determined to recite at least one abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claim 1, it is noted that the claim does not recite additional elements (i.e., claim 1 is a method that recites several disembodied steps since no device/hardware is relied on for performing the claimed limitations). Even if the “collecting” step is evaluated as an additional element, this step amounts at most to insignificant extra-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to independent claim 1, it is noted that the claim does not recite additional elements (i.e., claim 1 is a method that recites several disembodied steps). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Even if the “collecting” step is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) i.- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2 and 5-6 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-3 and 5-6 recite “wherein the first factors comprise: a duty duration, a duty start time, a duty end time, a take-off and landing time, a stop-over duration, a flight direction and crossing time zones, an airport difficulty, a pre-order duty and rest, a number of flight segments and staffing,” “wherein a method for obtaining the factor intensities comprises: carrying out the quantitative characterization on the first factors, and setting an upper limit ftop and a lower limit fdown of an influence of a quantitative characterization result on duty fatigue for first factors with not-normalized quantitative characterization; and carrying out a normalization processing on quantized first factors to obtain a factor intensity of the each of the first factors, “ “wherein a calculation method of the normalization processing is as follows:
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f wherein Q represents the factor intensity and f represents a characterization result,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mathematical Concepts” abstract idea groupings. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
31. 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.
32. 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.
33. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
34. 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.
35. Claims 1-2 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Rangan, Pub. No.: US 2020/0290740 A1, [hereinafter Rangan], in view of Konop, Pub. No.: US 2005/0154634 A1, [hereinafter Konop], in further view of Grube et al., Pub. No.: US 2016/0090097 A1, [hereinafter Grube].
As per claim 1, Rangan teaches a method for evaluating a flight string fatigue grade based on task information (paragraph 0005), comprising:
collecting task information of a flight string, and extracting first factors influencing a flight string fatigue degree based on the task information (paragraph 0005, discussing a method and systems for determining operator fatigue and that can be designed to reduce flight risk by evaluating the fatigue level of one or more flight crew members in real-time based on comprehensive and interrelated sets of fatigue indicators. For example, a pilot's expected level of fatigue throughout a flight can be predicted based on a variety of related risk factors and fatigue indicators; paragraph 0006, discussing that actions include… obtaining flight information associated with a flight on which the flight crew member will be present, the flight information indicating a series of expected events during the flight, determining, based on the history information and the flight information, a predicted fatigue profile for the flight crew member, determining, based on the predicted fatigue profile for the flight crew member, one or more fatigue indicator profiles for the flight crew member, the one or more fatigue indicator profiles representing expected values of one or more respective measurable fatigue indicators…; paragraph 0022, discussing a flight risk management framework is designed to reduce flight risk by evaluating the fatigue level of one or more flight crew members during a flight based on comprehensive and interrelated sets of risk factors; paragraph 0062, discussing that these factors determined from the flight crew member's schedule and other relevant factors can be processed by a trained machine learning model to predict the sleep of the flight crew member, which can then be used to predict fatigue for the flight crew member…; paragraphs 0061, 0066);
constructing an index evaluation system based on the first factors (paragraph 0067, discussing that a predicted fatigue profile for the flight crew member is determined based on the history information and the flight information. In some implementations, the various items of history information data and flight information data are weighted according to their relevance in predicting fatigue. For example, the types of history information and flight information that are more predictive of a flight crew member's fatigue may be weighted more heavily than the types of history information and flight information data that have a weaker correlation with flight crew member fatigue….; paragraph 0101, discussing that the fatigue indicator profiles are weighted or prioritized based on the strength of the correlation between the specific fatigue indicator and actual fatigue levels. These weightings can be applied to observed deviations between the measured fatigue indicator values and the predicted values in the respective fatigue indicator profiles to determine whether a threshold amount of deviation has occurred to trigger generation of an updated predicted fatigue profile. For example, fatigue indicators related to eye movement and heart rate can be more heavily weighted than other indicators as they may be more strongly correlated to fatigue. In such examples, higher weightings applied to deviations in the eye movement and heart rate of a flight crew member will be more likely to trigger generation of an updated fatigue indicator profile than another, lesser weighted, indicators. Weighting fatigue indicators based on the strength of the correlation between each indicator type and fatigue levels helps minimize the effects of outlier measured fatigue indicator values),
wherein constructing the index evaluation system comprises: classifying the first factors to obtain first-level indexes (paragraph 0073, discussing that the predicted fatigue profile for a flight crew member is obtained by comparing the history information for the respective flight crew member with the history information categories associated with each of the group fatigue profiles, and selecting the group fatigue profile for the category that most closely matches the history information for the respective flight crew member. For example, if the history information for the flight crew member indicates that the flight crew member has slept 6 hours in the last 24 hours, has flown 55 hours in the last seven days, and has not consumed any caffeine in the last 4 hours, then, based on the above-described group fatigue profile categories, the group fatigue profile generated for “Group 3” will be selected as the predicted fatigue profile of the flight crew member. In contrast, if, for example, the history information for the flight crew member indicates that the flight crew member has slept 9 hours in the last 24 hours, has flown 30 hours in the last seven days, and has consumed caffeine in the last 4 hours, then, based on the above-described group fatigue profile categories, the group fatigue profile generated for “Group 1′” will be selected as the predicted fatigue profile for the flight crew member; paragraph 0090, discussing that in some implementations, a K-means algorithm can be used to partition history information and/or the flight information for a flight crew member into clusters for time series analysis, and subsequent generation of a fatigue prediction profile and fatigue indicator profile(s)…In order to utilize the K-means algorithm, a value for the number of clusters (k) must be defined. Once k is defined, the k cluster centers are initialized, and each object (e.g., each previous flight performed by the flight crew member) is assigned to the nearest cluster center based upon the mapping the object) comprising time planning factors, environmental factors and workload factors (paragraph 0041, discussing that the flight management system stores flight information for a plurality of flights. For example, flight management system can store information that includes, but is not limited to, scheduled flight departure times, estimated flight arrival times, the identity of crew members scheduled to be on each flight, schedules for each aircraft, flight paths, departure airports, arrival airports, weather data, personnel schedules, personnel training databases, the expected level of familiarity the scheduled crew members have with the aircraft, standard protocol command databases, and a level of threat of each potential risk marker, such as adverse weather conditions, poses; paragraph 0060, discussing that the model also accounts for several factors that can be identified from a flight crew member's work schedule that affect sleep estimations. These factors predictive of fatigue include, but are not limited to, the start time of a flight the flight crew member has recently worked on, the length of a flight that the flight crew member has recently worked on, the length of time the flight crew member has been away from home, the local arrival time of a flight the flight crew member has recently worked on, commute and transit times experienced by the flight crew member between flights, the start time of the next flight the flight crew member is scheduled to work, the length of the next flight the flight crew member is scheduled to work, the number and length of opportunities that the flight crew member had to sleep between flights, the total number of flights that the flight crew member is scheduled to work during a predetermined time period, the change in time zones experienced by the flight crew member over a predetermined period, and the total amount of time between flights on which the flight crew member is scheduled to work; paragraph 0061, discussing that this model can account for environmental factor that influence sleep, such as global economic factors, geo political factors, weather, and global events; paragraph 0063), and
taking the first factors included in the first-level indexes as second-level indexes (paragraph 0090, discussing that in some implementations, a K-means algorithm can be used to partition history information and/or the flight information for a flight crew member into clusters for time series analysis, and subsequent generation of a fatigue prediction profile and fatigue indicator profile(s)…In order to utilize the K-means algorithm, a value for the number of clusters (k) must be defined. Once k is defined, the k cluster centers are initialized, and each object (e.g., each previous flight performed by the flight crew member) is assigned to the nearest cluster center based upon the mapping the object; paragraph 0111, discussing that in some implementations, the time series analysis and series clustering techniques described in reference to generating the predicted fatigue profile and the predicted fatigue indicator profiles can be used to generate the updated predicted fatigue profiles and the updated fatigue indicator profiles. For example, one or more of the fatigue indicator values measured during the flight can be analyzed using a time series analysis and, based on this analysis, assigned to a cluster in a time series mapping of the respective type of fatigue indicator values to levels of fatigue. Based on the clusters that the measure fatigue indicator values are assigned to, the predicted fatigue for the flight crew member can then be determined based on the fatigue level corresponding to the assigned clusters. If the fatigue indicator values are assigned to clusters that corresponds with a fatigue level that is different than the current predicted fatigue level for the flight crew member based on the predicted fatigue profile for the flight crew member, this indicates that the predicted fatigue profile for the flight crew member may need to be updated. In addition, dynamic time warping can be used to improve the accuracy of the updated predicted fatigue profiles and updated fatigue indicator profiles and project the predicted fatigue profile and predicted fatigue indicator values for the flight crew member into the future; paragraphs 0060, 0061), and
calculating a weight coefficient of each of the first factors (paragraph 0067, discussing that a predicted fatigue profile for the flight crew member is determined based on the history information and the flight information. In some implementations, the various items of history information data and flight information data are weighted according to their relevance in predicting fatigue. For example, the types of history information and flight information that are more predictive of a flight crew member's fatigue may be weighted more heavily than the types of history information and flight information data that have a weaker correlation with flight crew member fatigue….; paragraph 0101, discussing that the fatigue indicator profiles are weighted or prioritized based on the strength of the correlation between the specific fatigue indicator and actual fatigue levels. These weightings can be applied to observed deviations between the measured fatigue indicator values and the predicted values in the respective fatigue indicator profiles to determine whether a threshold amount of deviation has occurred to trigger generation of an updated predicted fatigue profile. For example, fatigue indicators related to eye movement and heart rate can be more heavily weighted than other indicators as they may be more strongly correlated to fatigue. In such examples, higher weightings applied to deviations in the eye movement and heart rate of a flight crew member will be more likely to trigger generation of an updated fatigue indicator profile than another, lesser weighted, indicators. Weighting fatigue indicators based on the strength of the correlation between each indicator type and fatigue levels helps minimize the effects of outlier measured fatigue indicator values),
carrying out a segment processing on the task information to obtain duty period segments (paragraph 0060, discussing that the model also accounts for several factors that can be identified from a flight crew member's work schedule that affect sleep estimations. These factors predictive of fatigue include, but are not limited to, the start time of a flight the flight crew member has recently worked on, the length of a flight that the flight crew member has recently worked on, the length of time the flight crew member has been away from home, the local arrival time of a flight the flight crew member has recently worked on, commute and transit times experienced by the flight crew member between flights, the start time of the next flight the flight crew member is scheduled to work, the length of the next flight the flight crew member is scheduled to work, the number and length of opportunities that the flight crew member had to sleep between flights, the total number of flights that the flight crew member is scheduled to work during a predetermined time period, the change in time zones experienced by the flight crew member over a predetermined period, and the total amount of time between flights on which the flight crew member is scheduled to work; paragraph 0065, discussing that the history information can include the measured fatigue indicator values and the fatigue profiles generated for the flight crew member over several flights worked by the flight crew member over a predetermined period of time (e.g., previous 12 hours, previous 24 hours, etc.). FIG. 7 depicts a fatigue profile for a flight crew member generated over a predetermined period of time (e.g., the previous 24 hour time period) that includes three fatigue profiles generated during three flights worked by the flight crew member during the time period, as well as the predicted fatigue levels of the flight crew member during periods of rest between each of the flights; paragraph 0071, discussing that a plurality of test subjects can submit their history information, including flight schedules for a specified period of time; paragraph 0080, discussing that the set of correlations between fatigue level and measured fatigue indicators is generated by measuring the actual fatigue levels and one or more fatigue indicator values for a plurality of test subjects over a period of time (e.g., during a flight) to determine a set of correlations between the measured fatigue levels and measured fatigue indicator values; paragraph 0086, discussing that example types of data that can be analyzed using time series analysis to train a fatigue prediction machine learning model include, but are not limited to, data regarding whether a flight crew member was awake or asleep during portions of a duty schedule, the predicted fatigue for a flight crew member over the course of a duty schedule of one or more flights, measured alertness of a flight crew member over the course of a duty schedule of one or more flights, and the predicted risk for a flight; paragraphs 0022, 0063, 0067, 0074, 0084);
carrying out a quantitative characterization on the each of the first factors to obtain factor intensities of the duty period segments (paragraph 0069, discussing that the predicted fatigue profile also includes a threshold fatigue level. In some implementations, the threshold fatigue level represents a level of fatigue above which a flight crew member's ability to manage a critical event is diminished. In some implementations, the threshold fatigue level is dynamic and is adjusted based on the estimated level of fatigue required for each portion of the flight. For example, during critical events, such as takeoff and landing, the threshold fatigue level can be higher compared to other portions of the flight to account for the increased flight crew attention required during the critical events. FIG. 6 depicts a dynamic fatigue threshold that is adjusted based on the level of risk, and corresponding attention required, for various portions of a flight);
calculating first fatigue values of the duty period based on weight coefficients and the factor intensities (paragraph 0022, discussing evaluating the fatigue level of one or more flight crew members during a flight based on comprehensive and interrelated sets of risk factors. For example, the flight risk management framework can be configured to predict a flight crew member's expected level of fatigue throughout a flight based on a variety of related risk factors, and, in response, can provide mitigation techniques to reduce the flight risk posed by the flight crew member's fatigue level; paragraph 0065, discussing that the history information can include the measured fatigue indicator values and the fatigue profiles generated for the flight crew member over several flights worked by the flight crew member over a predetermined period of time. FIG. 7 depicts a fatigue profile for a flight crew member generated over a predetermined period of time that includes three fatigue profiles generated during three flights worked by the flight crew member during the time period, as well as the predicted fatigue levels of the flight crew member during periods of rest between each of the flights. The fatigue profiles are generated based on fatigue indicator sensor data collected during the respective flights. The accuracy of the predicted fatigue profile generated for the flight crew member for an upcoming flight can be improved, by, for example, including one or more of the fatigue profiles generated during recent flights worked by the flight crew member, as well as the measured fatigue indicator values corresponding to the fatigue profiles in the data analyzed by the machine learning model to generate a predicted fatigue profile; paragraph 0067, discussing that a predicted fatigue profile for the flight crew member is determined based on the history information and the flight information. In some implementations, the various items of history information data and flight information data are weighted according to their relevance in predicting fatigue. For example, the types of history information and flight information that are more predictive of a flight crew member's fatigue may be weighted more heavily than the types of history information and flight information data that have a weaker correlation with flight crew member fatigue…; paragraph 0074, discussing that one or more fatigue indicator profiles are generated for the flight crew member. FIG. 4B depicts exemplary fatigue indicator profiles. The one or more fatigue indicator profiles depict the predicted fatigue indicator values for a user for each of the respective fatigue indicators over a period of time. In some implementations, the one or more fatigue indicator profiles generated for the flight crew member represent the expected values of one or more measurable fatigue indicators for the flight crew member over the duration of the flight based on the crew members predicted fatigue profile. For example, fatigue indicator profile depicts the expected eyelid closure rate for a pilot over the duration of the flight and fatigue indicator profile depicts the expected standard protocol command error rate for the pilot over the duration of the flight) and
calculating a second fatigue value of the whole flight string (paragraph 0005, discussing method and systems for determining operator fatigue and that can be designed to reduce flight risk by evaluating the fatigue level of one or more flight crew members (e.g., pilots) in real-time based on comprehensive and interrelated sets of fatigue indicators. For example, a pilot's expected level of fatigue throughout a flight can be predicted based on a variety of related risk factors and fatigue indicators, and, in response, flight risk mitigation interventions can be provided to reduce the flight risk posed by the pilot's predicted fatigue levels; paragraph 0022, discussing that a flight risk management framework is designed to reduce flight risk by evaluating the fatigue level of one or more flight crew members during a flight based on comprehensive and interrelated sets of risk factors. For example, the flight risk management framework can be configured to predict a flight crew member's expected level of fatigue throughout a flight based on a variety of related risk factors, and, in response, can provide mitigation techniques to reduce the flight risk posed by the flight crew member's fatigue level); and
determining a flight string fatigue grade based on the second fatigue value (paragraph 0022, discussing that a flight risk management framework is designed to reduce flight risk by evaluating the fatigue level of one or more flight crew members during a flight based on comprehensive and interrelated sets of risk factors. For example, the flight risk management framework can be configured to predict a flight crew member's expected level of fatigue throughout a flight based on a variety of related risk factors, and, in response, can provide mitigation techniques to reduce the flight risk posed by the flight crew member's fatigue level; paragraph 0070, discussing that the fatigue prediction model accounts for the time of day during which the flight will occur in predicting the level of fatigue for a flight crew member. In some implementations, a two-process fatigue prediction model is applied to the flight crew member's sleep history to determine the flight crew member's predicted level of fatigue throughout the flight.).
While Rangan teaches calculating first fatigue values of the duty period based on weight coefficients and the factor intensities, it does not explicitly teach calculating first fatigue values of the duty period segments, that the second fatigue value of the whole flight string is based on the first fatigue values, and determining a flight string fatigue by assigning the flight string fatigue grade to one of a plurality of fatigue grades consisting of Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value. Konop in the analogous art of fatigue assessment systems teaches:
calculating first fatigue values of the duty period segments (paragraph 0024, discs using that he human Factors Scheduling Safety System has been devised for the purpose of specifically addressing the impact of human fatigue in schedule evaluation, formulation, and execution. For each work situation and environment multiple factors, that negatively impact human alertness levels, are evaluated. The relative level of this impact is then calculated in terms of the available and evolving research, and numeric values are assigned to each Fatigue Factor according to its relative impact. Fatigue limits, or rules, are then established in terms Fatigue Factor accumulations as might affect safety and/or productivity. Finally, the quality and quantity of subsequent rest is evaluated in similar terms, in a determination of the amelioration of previously accumulated fatigue, and in terms of subsequent scheduled work periods. It is a scheduling system which evaluates multiple factors, relative impact, total fatigue accumulation, and intervening rest amelioration; paragraph 0006, discussing that although a number of other Fatigue Factors may be computed and evaluated in a more precise computer based Scheduling Safety System, or additional values computed based upon regulatory or contractual requirements, the remaining computations on the Fatigue Factors Computational Worksheet simply involve summations. The Total Fatigue Factors accumulated during a given period of duty is computed as the sum of Fatigue Factors Worksheet (FIG. 7) Lines 1 through 8. (In a computer based system, the tally for Sum Registers AA through HH are added to obtain the Total Fatigue Factor Accumulation). This can be accomplished by the following algorithm, or some variation; FIG. 7); and
calculating a second fatigue value of the whole flight string based on the first fatigue values (paragraph 0006, discussing that although a number of other Fatigue Factors may be computed and evaluated in a more precise computer based Scheduling Safety System, or additional values computed based upon regulatory or contractual requirements, the remaining computations on the Fatigue Factors Computational Worksheet simply involve summations. The Total Fatigue Factors accumulated during a given period of duty is computed as the sum of Fatigue Factors Worksheet (FIG. 7) Lines 1 through 8. (In a computer based system, the tally for Sum Registers AA through HH are added to obtain the Total Fatigue Factor Accumulation). This can be accomplished by the following algorithm, or some variation; paragraph 0058, discussing summing the numeric value of the cumulative Fatigue Factors present in the individual work task or duty assignment; FIG. 7, element 9 and 10, illustrating a Fatigue Factors Computational Worksheet; paragraph 0143, discussing that although any number of additional Fatigue Factor variables might be generated and evaluated by the Scheduling Safety System, the last of the Fatigue Factors considered within the Sample Embodiment are the Fatigue Factors for "Other Duty" performed by the flight crewmember).
Rangan is directed towards a method and system for mitigating operational risk in aircraft. Konop is directed to a human factors scheduling safety system. Therefore they are deemed to be analogous as they both are directed towards employee evaluation and task management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rangan with Konop because the references are analogous art because they are both directed to solutions for employee evaluation and task management, which falls within applicant’s field of endeavor (scheduling data processing and employee fatigue evaluation), and because modifying Rangan to include Konop’s features for including calculating first fatigue values of the duty period segments and calculating a second fatigue value of the whole flight string based on the first fatigue values, in the manner claimed, would serve the motivation of ensuring that working personnel will not accumulate fatigue which will exceed predetermined levels, beyond those which are safe and/or efficient (Konop at paragraph 0005); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
While Rangan teaches determining a flight string fatigue grade based on the second fatigue value and describes fatigue levels above threshold level (paragraph 0120), the Rangan-Konop combination does not explicitly teach by assigning the flight string fatigue grade to one of a plurality of fatigue grades consisting of Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value. However, Grube in the analogous art of fatigue analysis systems teaches this concept. Grube teaches:
by assigning the flight string fatigue grade to one of a plurality of fatigue grades consisting of Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value (paragraph 0012, discussing that an airline dispatcher may monitor the fatigue levels of various pilots in real time and use the fatigue levels to determine whether a future flight assigned to a particular pilot should be reassigned to a different pilot due to increasing levels of fatigue; paragraph 0014, discussing that in some instances, the fatigue level may be known and/or assumed. For example, a pilot flying his first mission after several days of rest may be considered to be fully rested (i.e., not fatigued). By contrast, a pilot flying his last mission at the end of his duty period may be considered to be fully fatigued. In various other instances, the fatigue level of the vehicle operator may be determined by querying the vehicle operator to rate his fatigue level (e.g., on a scale of one to ten) when biometric data is gathered. Each instance in which the vehicle operator rates his fatigue level may be imprecise because the rating is subjective. For example, a vehicle operator may be queried to rate his fatigue level at three separate instances in which his fatigue level is the same. However, he may rate his fatigue level as a “four out of ten” in the first instance, a “six out of ten” in the second instance, and a “five out of ten” in the third instance. These three subjectively-rated fatigue levels average to a “five out of ten” fatigue level. As additional instances are gathered for the statistical model for a particular level of fatigue, the vehicle operator's cumulative subjective ratings of fatigue level for the particular level of fatigue may average to a number that is approximately correct. Put differently, if the vehicle operator's subjective fatigue level rating is high (relative to his actual fatigue level) as often as it is low, then an average subjective fatigue level rating may be approximately equal to the particular fatigue rating. Many statistical models that result from data points from multiple instances may inherently compensate for such subjective ratings. For example, if two instances that have identical biometric data are subjectively rated as a “four out of ten” and a “six out of ten,” respectively, by the operator, then coefficients of a resulting linear regression statistical model may be the approximately equal to coefficients of the linear regression model if the operator had rated both instances as a “five out of ten.”… As the vehicle operator becomes more fatigued, he may not operate the vehicle with as much precision (e.g., a pilot drifting from a landing glide path or a truck driver veering out of his travel lane). A vehicle operator may be fully fatigued when his reaction time reaches or exceeds a predetermined threshold (e.g., a threshold reaction time that may be considered unsafe). Successively faster response times may be associated with successively lower fatigue levels; paragraph 0015, discussing that the determined fatigue level of the operator can be normalized based on the operator's measured responses becoming too slow and/or inaccurate. For example, on a scale of zero to ten, where zero is fully rested and ten is fully fatigued, an operator's fatigue level can be determined to be ten when his responses become unacceptably slow, incorrect, and/or inaccurate. For example, the operator's fatigue level can be determined to be zero when his responses are equal to his personal fastest response times. Fatigue levels between zero and ten can be determined based on the operator's response times being between his fastest time and slowest time (e.g., using a linear interpolation method). As another example, the operator's fatigue level may be determined to be ten if he fails to correctly perform a particular task and/or routine required to be performable by an operator of the vehicle. For example, pilots must be able to perform certain emergency procedures, such as properly reacting to the loss of power of an engine during takeoff. If a pilot cannot properly perform such a procedure in a simulator environment due to fatigue, then the pilot's fatigue level may be determined to be a ten; paragraph 0018, discussing that in an aircraft, a warning about fatigue for a first pilot could be communicated to a second pilot. Likewise, the warning could be communicated to a flight attendant on board the aircraft. The threshold level can vary from one vehicle operator to the next. Also, different operators may use different threshold levels. For example, first airline may use a threshold of eight on a scale of one to ten whereas a second airline may use a threshold of seven on the same scale; paragraph 0020).
The Rangan-Konop combination describes features related to employee evaluation. Grube is directed to fatigue analysis systems. Therefore they are deemed to be analogous as they both are directed towards fatigue evaluation. 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 Rangan-Konop combination with Grube because the references are analogous art because they are both directed to solutions for employee evaluation, which falls within applicant’s field of endeavor (scheduling data processing and employee fatigue evaluation), and because modifying the Rangan-Konop combination to include Grube’s feature for including by assigning the flight string fatigue grade to one of a plurality of fatigue grades consisting of Grade I, Grade II, Grade III and Grade IV according to predetermined threshold ranges of the second fatigue value, in the manner claimed, would serve the motivation of effectively providing real-time or near-real-time monitoring of the vehicle operator's fatigue level (Grube at paragraph 0017); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Rangan-Konop-Grube combination teaches the method for evaluating the flight string fatigue grade based on task information according to claim 1. Rangan further teaches wherein the first factors comprise: a duty duration, a duty start time, a duty end time, a take-off and landing time, a stop-over duration, a flight direction and crossing time zones, an airport difficulty, a pre-order duty and rest, a number of flight segments and staffing (paragraph 0007, discussing that the sleep history for the flight crew member includes a number of hours the flight crew member has slept in the prior 24 hours;… the one or more updated fatigue indicator profiles include a plurality of predicted fatigue indicator values for a duration of the flight; paragraph 0041, discussing that the flight management system can store information that includes, but is not limited to, flight identification numbers, scheduled flight departure times, estimated flight arrival times, the identity of crew members scheduled to be on each flight, schedules for each aircraft, flight paths, departure airports, arrival airports, weather data, personnel schedules, personnel training databases, the expected level of familiarity the scheduled crew members have with the aircraft, standard protocol command databases, and a level of threat of each potential risk marker, such as adverse weather conditions, poses; paragraph 0059, discussing that the model accounts for time of day, including time shifting events, such as changing time zones, and jet lag; paragraph 0060, discussing that these factors predictive of fatigue include, but are not limited to, the start time of a flight the flight crew member has recently worked on, the length of a flight that the flight crew member has recently worked on, the length of time the flight crew member has been away from home, the local arrival time of a flight the flight crew member has recently worked on, commute and transit times experienced by the flight crew member between flights, the start time of the next flight the flight crew member is scheduled to work, the length of the next flight the flight crew member is scheduled to work, the number and length of opportunities that the flight crew member had to sleep between flights, the total number of flights that the flight crew member is scheduled to work during a predetermined time period, the change in time zones experienced by the flight crew member over a predetermined period, and the total amount of time between flights on which the flight crew member is scheduled to work; paragraph 0068, discussing that the predicted fatigue profile can include critical event indicators for portions of the flight during which a critical event is occurring. Critical events can include, but are not limited to, take-off, landing, expected hazardous weather, changes in airport conditions, and flight through crowded airspace; paragraphs 0069, 0074).
Examiner notes that Konop, in addition to Rangan, as cited above, also teaches: wherein the first factors comprise: a duty duration, a duty start time, a duty end time, a take-off and landing time, a flight direction and crossing time zones, a pre-order duty and rest, a number of flight segments and staffing (paragraph 0067, discussing that accepted research proves that, beyond the normal flight hours limitations, flight crewmember fatigue is greatly affected by the number of operating cycles (takeoffs and landings), time on duty, time of day on duty, time zone changes, rest, and even nutritional and hydration factors. Each of the recognized Fatigue Factors can be accommodated in a system which addresses varying mixes and levels of Fatigue Factors, beyond the normal requirement to limit flight hours and provide minimum periods of rest….; paragraphs 0080, 0087, 0091; FIG. 7).
As per claim 5, the Rangan-Konop-Grube combination teaches the method for evaluating the flight string fatigue grade based on task information according to claim 1. Rangan further teaches wherein a method for obtaining the factor intensities comprises: carrying out the quantitative characterization on the first factors, and setting an upper limit ftop and a lower limit fdown of an influence of a quantitative characterization result on duty fatigue for first factors with not-normalized quantitative characterization (paragraph 0067, discussing that a predicted fatigue profile for the flight crew member is determined based on the history information and the flight information . In some implementations, the various items of history information data and flight information data are weighted according to their relevance in predicting fatigue. For example, the types of history information and flight information that are more predictive of a flight crew member's fatigue may be weighted more heavily than the types of history information and flight information data that have a weaker correlation with flight crew member fatigue; paragraph 0069, discussing that the predicted fatigue profile also includes a threshold fatigue level. In some implementations, the threshold fatigue level represents a level of fatigue above which a flight crew member's ability to manage a critical event is diminished. In some implementations, the threshold fatigue level is dynamic and is adjusted based on the estimated level of fatigue required for each portion of the flight. For example, during critical events, such as takeoff and landing, the threshold fatigue level can be higher compared to other portions of the flight to account for the increased flight crew attention required during the critical events. FIG. 6 depicts a dynamic fatigue threshold that is adjusted based on the level of risk, and corresponding attention required, for various portions of a flight; paragraph 0101, discussing that the fatigue indicator profiles are weighted or prioritized based on the strength of the correlation between the specific fatigue indicator and actual fatigue levels. These weightings can be applied to observed deviations between the measured fatigue indicator values and the predicted values in the respective fatigue indicator profiles to determine whether a threshold amount of deviation has occurred to trigger generation of an updated predicted fatigue profile. For example, fatigue indicators related to eye movement and heart rate can be more heavily weighted than other indicators as they may be more strongly correlated to fatigue. In such examples, higher weightings applied to deviations in the eye movement and heart rate of a flight crew member will be more likely to trigger generation of an updated fatigue indicator profile than another, lesser weighted, indicators. Weighting fatigue indicators based on the strength of the correlation between each indicator type and fatigue levels helps minimize the effects of outlier measured fatigue indicator values; paragraphs 0103, 0108).
Rangan does not explicitly teach carrying out a normalization processing on quantized first factors to obtain a factor intensity of the each of the first factors. However, Konop in the analogous art of fatigue assessment systems teaches this concept. Konop teaches:
carrying out a normalization processing on quantized first factors to obtain a factor intensity of the each of the first factors (paragraph 0024, discussing that the Human Factors Scheduling Safety System has been devised for the purpose of specifically addressing the impact of human fatigue in schedule evaluation, formulation, and execution. For each work situation and environment multiple factors, that negatively impact human alertness levels, are evaluated. The relative level of this impact is then calculated in terms of the available and evolving research, and numeric values are assigned to each Fatigue Factor according to its relative impact. Fatigue limits, or rules, are then established in terms Fatigue Factor accumulations as might affect safety and/or productivity. Finally, the quality and quantity of subsequent rest is evaluated in similar terms, in a determination of the amelioration of previously accumulated fatigue, and in terms of subsequent scheduled work periods. It is a scheduling system which evaluates multiple factors, relative impact, total fatigue accumulation, and intervening rest amelioration).
Rangan is directed towards a method and system for mitigating operational risk in aircraft. Konop is directed to a human factors scheduling safety system. Therefore they are deemed to be analogous as they both are directed towards employee evaluation and task management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Rangan with Konop because the references are analogous art because they are both directed to solutions for employee evaluation and task management, which falls within applicant’s field of endeavor (scheduling data processing and employee fatigue evaluation), and because modifying Rangan to include Konop’s feature for including carrying out a normalization processing on quantized first factors to obtain a factor intensity of the each of the first factors, in the manner claimed, would serve the motivation of ensuring that working personnel will not accumulate fatigue which will exceed predetermined levels, beyond those which are safe and/or efficient (Konop at paragraph 0005); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Allowable Subject Matter
36. Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claim 6 recites “The method for evaluating the flight string fatigue grade based on task information according to claim 5, wherein a calculation method of the normalization processing is as follows:
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f wherein Q represents the factor intensity and f represents a characterization result.” While Everman discloses general techniques for normalizing or scaling factors in a fatigue or physiological assessment context. Everman does not disclose the particular piecewise linear calculation recited in claims 6, nor does it apply such normalized factor intensities in an index evaluation system for flight string fatigue. Further, the other cited references, including Rangan and Konop, do not teach or suggest the claimed normalizing calculation method. The prior art of record either individually or in combination does not teach “wherein a calculation method of the normalization processing is as follows:
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f wherein Q represents the factor intensity and f represents a characterization result”, as recited in claim 6. Claim 6 is not allowable, however, because claim 6 remains rejected under 35 U.S.C. 101. Furthermore, even if the §101 rejection of claim 6 is overcome, claim 6 would be objected to as being dependent upon a rejected base claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Meeker et al., Pub. No.: US 2023/0309854 A1 – describes a monitoring engine that uses statistical averages, deviations, etc. from a data set obtained from a plurality of pilots and associated flights in order to construct the thresholds. The thresholds are correlated with physiological danger and/or fatigue thresholds for the pilot, such that values that meet or deviate from the thresholds (e.g., fall below a lower value or exceed an upper value) can indicate danger of physiological fatigue or more significant health risk to the pilot.
Marche et al., Pub. No.: US 2023/0211789 A1 – describes a system for monitoring a fatigue level of an operator of a vehicle, the system including one or more controllers configured to determine the fatigue level of the operator by analyzing the received signal using an algorithm developed using operator fatigue statistics; generate a real-time fatigue report for the operator based on the determined fatigue level of the operator. Further describes that the fatigue level may be provided as a number from 1 to 5.
Seko et al., Patent No.: US 4,602,247 – describes a method and system for detecting driver fatigue.
Kaplan et al., Patent No.: US 5,813,993 – describes an alertness and drowsiness detection and tracking system.
Thomas, Lisa C., et al. "Fatigue detection in commercial flight operations: Results using physiological measures." Procedia Manufacturing 3 (2015): 2357-2364 – describes evaluating the effects of varying levels of fatigue and workload on pilot performance and physiological responses.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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