Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 (i.e., changing from AIA to pre-AIA ) 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.
DETAILED ACTION
The following NON-FINAL Office Action is in response to Application 18/584,944 - filed on
2/22/2024.
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
The Examiner has noted the Applicants claiming Priority from Provisional Application 63/486,994 filed 02/26/2023.
Status of Claims
Claims 1-20 are currently pending of which:
Claims 1-20 are currently under examination and have been rejected as follows.
Claim Objections
Claims 1, 19 are objected to because of the following informalities:
Claim 1 recites: A system for tracking and optimizing one or more vehicle operators performance [bolded emphasis added].
Claim 1 is recommended to recite, as an example only: A system for tracking and optimizing one or more vehicle operators’ performance.
Claim 19 recites: A computer-implemented method to tracking and optimizing one or more vehicle operators performance [bolded emphasis added].
Claim 19 is recommended to recite, as an example only: A computer-implemented method for tracking and optimizing one or more vehicle operators’ performance.
Appropriate corrections are required.
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Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
In this instant case:
Claim 1 is a “system” claim which recites: “one or more computer-executable instructions configured to assign one or more segments of one or more routes and one or more vehicles to the one or more vehicle operators for one or more trips”.
Claim 2 is a “system” claim which recites: “one or more computer-executable instructions configured to determine the anticipated physical and mental health based on one or more stored weight profiles”.
Claim 5 is a “system” claim which recites: “one or more computer-executable instructions configured to assign the one or more segments of the one or more routes and the one or more vehicles to the one or more vehicle operators further based on stored one or more maintenance histories associated with the one or more vehicles”.
Claim 8 is a “system” claim which recites: “one or more computer-executable instructions configured to determine the extent of the one or more performance impacting factors…”.
Claim 9 is a “system” claim which recites: “one or more computer-executable instructions configured to determine ability of the one or more vehicle operators to reduce the impact of the extent of the one or more performance impacting factors…”.
Claim 10 is a “system” claim which recites: “one or more computer-executable instructions configured to determine the anticipated physical and mental health of the one or more vehicle operators while operating the one or more vehicles…”.
Claim 11 is a “system” claim which recites: “one or more computer-executable instructions configured to check that the assigned one or more segments of the one or more routes and the one or more vehicles to the one or more vehicle operators for the one or more trips comply with the one or more of the following by using one or more stored rules…”.
Claim 12 is a “system” claim which recites: “one or more computer-executable instructions configured to receive and store data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed”.
Claim 13 is a “system” claim which recites: “one or more computer-executable instructions configured to check that the received and stored data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed, comply with the one or more of the following by using one or more stored rules…”.
Claim 14 is a “system” claim which recites: “one or more computer-executable instructions configured to auto-adjust the received and stored data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed data”.
Claim 16 is a “system” claim which recites: “one or more computer-executable instructions configured to initiate one or more workflows for one or more manual adjustments to the received and saved trips data when one or more stored predefined conditions are satisfied”.
Claim 17 is a “system” claim which recites: “one or more computer-executable instructions configured to generate one or more of the following related to the received and stored trips data…”.
Claim 18 is a “system” claim which recites: “one or more computer-executable instructions configured to provide one or more workflows allowing interaction through one or more user graphical interfaces …”.
Claim 20 is a “system” claim which recites: “A non-transitory computer readable storage medium with an executable program stored thereon, wherein the executable program is configured to instruct one or more processors to computer-implemented method of Claim 19”.
The Examiner interprets computer-executable instructions and executable program as generic placeholders followed by their respective functions of: assign, determine, check, receive, store, use, auto-adjust, initiate, generate, provide, instruct and further not modified by sufficient structure. Thus, it appears that independent Claims 1, 19 and dependent Claims 2-18, 20 invoke 35 USC 112(f).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-18 are directed to a system or machine which is a statutory category.
Claims 19 is directed to a method or process which is a statutory category.
Claim 20 is directed to a non-transitory computer readable storage medium or article of manufacture which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth mitigating risk, agreements in the form of contracts, and legal obligations including: “assign one or more segments of one or more routes and one or more vehicles to the one or more vehicle operators for one or more trips based on: anticipated physical and mental health of the one or more vehicle operators… extent of one or more performance impacting factors… and ability of the one or more vehicle operators to reduce impact of… performance impacting factors”. Assessing health and ability risk of vehicle operators, assigning routes, and following rules, laws, and regulations fall within mitigating risk as it pertains to fundamental economic principles, and agreements in the form of contracts and legal obligations as they pertain to commercial or legal interactions, each under the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claims 1,19 recite the following additional elements: “system”, “processors”, “memories”, “computer-executable instructions”, and “computing devices”. The functions of these additional elements include examples such as “assign one or more segments… to the one or more vehicle operators”, “based on… anticipated physical and mental health of the one or more vehicle operators”, “based on… extent of one or more performance impacting factors”, and “based on… ability of the one or more vehicle operators to reduce impact of the extent”. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of gathering, evaluating, communicating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 8-10 recite the additional element “stored model”. Dependent claims 8-11, 13 recite the additional element “self-learning algorithms”. Dependent claim 20 recites the additional elements “non-transitory computer readable storage medium” and “executable program”. The functions of these additional elements include examples such as storing rules, determining extent of performance impacting factors, storing insights, determining ability of operators to reduce impact of factors, determining physical and mental health, etc. The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of gathering, evaluating, communicating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. The additional element “self-learning model” language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea.
Further, dependent claims 2-7, 12, 14-18 merely incorporate the additional elements recited in claims 1, 19 along with further narrowing of the abstract idea of claims 1, 19 and their execution of the abstract idea. Specifically, the dependent claims narrow the “system”, “processors”, “memories”, “computer-executable instructions”, and “computing devices” to capabilities such as determine, store, receive, relate, specify, include, assign, base on, reduce, use, and auto-adjust various forms of data such as weight profiles, slept hours, various time lengths, time limits, use history, incidents, disabilities, traumas, breaks, accidents, organizations, fleet types, market segments, operator groups, trip types, regions, vehicle models, route conditions, maintenance history, rules, laws, regulations, insights, predictions, recommendations, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-20 are reasoned to be patent ineligible.
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REJECTIONS BASED ON PRIOR ART
Examiner Note: Some rejections will contain bracketed comments preceded by an “EN” that will denote an examiner note. This will be placed to further explain a rejection.
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Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 4-14, 17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Kan et al. US 20170154394 A1, hereinafter Kan in view of
Meroux et al. US 20220252415 A1, hereinafter Meroux. As per,
Regarding Claims 1, 19: Kan teaches:
A system for (claim 1) / computer-implemented method to (claim 19) tracking and optimizing one or more vehicle operators performance (Kan ¶ [0006]: …the presently disclosed invention comprises one or more route planning algorithms that generate a trip schedule for a driver that will optimize for compliance with hours-of-service requirements, waypoint requirements, and other driving factors such as fatigue, driving conditions, delivery efficiency, fuel economy, other economic factors, and/or the like. End-¶ [0042]: Trip data may also comprise, according to particular embodiments, a trip name or identifier and any other information that would assist managers to track and to analyze operational performance on a given trip), the system comprising (claim 1) / executed by one or more computing devices each of which comprising of (claim 19):
one or more processors coupled to one or more memories (Kan ¶ [0032-0033]);
one or more computer-executable instructions configured to assign (claim 1) / the method comprising assigning (claim 19) one or more segments of one or more routes [..] to the one or more vehicle operators for one or more trips (Kan ¶ [0072]: Method 150 then iterates through all nodes (i.e., all intersections between two route segments) in the step-151 initialized trip schedule and then iterates through each route plan generated or otherwise assigned to the trip under consideration, in step 106 of method 100 (FIG. lA) as reflected in the nested iterative logic of steps 160 (process all nodes) and 161 (process all route plans), respectively. The set of all route plans processed in method 150 and not discarded therein (in either step 157 or step 159) becomes the set of generated trip schedules, as previously identified, e.g., by step 108 of method 100 (FIG. lA). [Also see Figs. 1A, 1B and related text]) based on:
anticipated physical and mental health of the one or more vehicle operators while operating the one or more vehicles during the one or more trips (Kan ¶ [0090]: Method 100 may then proceed to step 110 wherein one or more fatigue levels may be determined for the driver at one or more times during the one or more step-108 generated trip schedules, in accordance with particular embodiments. In particular (non-limiting) embodiments, step-110 determined fatigue levels are determined by applying the step-103 received neurobehavioral performance model to the step-109 determined driver sleep schedule. In particular (non-limiting) embodiments, step-109 determined fatigue levels are determined for a particular time granularity level (e.g., every 10 mins, every hour, etc.) for the entire duration of a driving trip);
extent of one or more performance impacting factors associated with the one or more segments of the one or more routes and ability of the one or more vehicle operators to reduce impact of the extent of the one or more performance impacting factors during the one or more trips (Kan ¶ [0094]: Method 100 may then enter into optional steps 114 and 115, wherein the step-108 generated trip schedule is modified in one or more ways, in accordance with particular embodiments. Modification of the step-108 generated trip schedule in optional step 114 may comprise (without limitation): the insertion of a rest interval between driving two segments, delaying a start time from one or more segments, making adjustments [EN: reducing impact] for slower-than-expected segment travel times, and/or the like. According to particular embodiments, modifying the determined trip schedule comprises one or more of: advancing or delaying a start time or end time of the trip schedule or a driving segment thereof; inserting or removing an off-duty segment, on-duty segment, and/or sleeper segment into the trip schedule; and/or advancing or delaying a start time or an end time of an off-duty segment, an on-duty segment, and/or a sleeper segment in the trip schedule).
Although Kan teaches assigning segments, routes, and vehicle operators to trips, Kan does not specifically teach assigning a vehicle to the trip.
However, Meroux in analogous art of optimizing travel routes for vehicle operators teaches or suggests:
[..] assign [..] routes and one or more vehicles [..] for one or more trips (Meroux ¶ [0021]: The shortcomings in the two vehicle assignment procedures described above may be addressed by an optimized vehicle assignment procedure that is based on evaluating various factors prior to assigning various vehicles to various travel routes. The various vehicles that are assigned to the various travel routes in accordance with the optimized vehicle assignment procedure are shown in column 315 of the table 300. [Also see Fig. 3 and related text]).
Meroux and Kan are found as analogous art of optimizing travel routes for vehicle operators. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kan’s optimized routing and tracking system and method to have included Meroux’s teachings around assigning a vehicle to the trip in addition to assigning operators, segments, and routes. The benefit of these additional features would have mitigated risk by further optimizing trip plans based on assigning vehicles best suited for specific routes and operators. (Meroux ¶ []). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kan in view of Meroux (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of optimizing travel routes for vehicle operators. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kan in view of Meroux above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding Claim 4: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
wherein the one or more performance impacting factors associated with the one or more segments of the one or more routes include one or more of the following:
one or more weather conditions; one or more traffic conditions; one or more speed conditions; (Kan mid-¶ [0069]: The step-154 determined driving segment start and end times may be retrieved from a database, calculated using heuristic rules (e.g., assume a 50 MPH speed); modified to account for daily traffic patterns, time of day, weather conditions, seasonal traffic patterns, and/or the like; or retrieved from another technology system; and/or the like);
[..];
one or more parking conditions (Kan Claim 20: the received optimization data comprises one or more of: fuel costs, toll costs, fatigue, probability of on-time pickup and delivery, probability of finding parking spots when arriving at truck stop);
[..].
Regarding Claim 5: Kan / Meroux teaches all the limitations of claim 1 above.
Although Kan teaches assigning segments, routes, and vehicle operators to trips, Kan does not specifically teach assigning a vehicle to the trip based on maintenance history of the vehicle.
However, Meroux in analogous art of optimizing travel routes for vehicle operators teaches or suggests:
further comprising one or more computer-executable instructions configured to assign the one or more segments of the one or more routes and the one or more vehicles to the one or more vehicle operators further based on stored one or more maintenance histories associated with the one or more vehicles (Meroux ¶ [0021]: The software application 107 may obtain vehicle maintenance records system 115 of a vehicle, such as, for example, maintenance records of the gasoline vehicle 135. Some examples of such records may include oil changes, tire replacement, parts replacements, mileage, and/or condition of various parts of the vehicle (coolant system, transmission, alternator, etc.). In some implementations, various conditions of the gasoline vehicle 135 may be dynamically modified in the vehicle maintenance records system 115 at various times such as, for example, prior to deployment on a travel route, when traveling on a travel route, upon reaching a destination, etc. The dynamically updated conditions can include, for example, tire pressure, oil level, coolant level, and battery parameters. In the case of electric vehicles, the vehicle maintenance records system 115 may provide information such as, for example, software updates carried out upon the vehicle computer 121 of the first electric vehicle 120, software and/or hardware issues in the vehicle computer 121 of the first electric vehicle 120, and/or a performance history of the vehicle computer 121 of the first electric vehicle 120 (crashes, malfunctions, security issues, etc.). ¶ [0022]: In an operation in accordance with the disclosure, the software application 107 may use the information obtained from the various sources (such as the vehicle battery database 110 and the vehicle maintenance records system 115) to assign travel routes to some or all of the vehicles of the fleet).
Meroux and Kan are found as analogous art of optimizing travel routes for vehicle operators. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kan’s optimized routing and tracking system and method to have included Meroux’s teachings around assigning a vehicle to the trip based on maintenance history of the vehicle. The benefit of these additional features would have mitigated risk by further optimizing trip plans based on assigning vehicles best suited for specific routes and operators. (Meroux ¶ []). The predictability of such modifications and/or variations, would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kan in view of Meroux (see MPEP 2143 G).
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of optimizing travel routes for vehicle operators. In such combination each element would have merely performed the same function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements, as evidenced by Kan in view of Meroux above, the to- be combined elements would have fit together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (see MPEP 2143 A).
Regarding Claim 6: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
wherein the extent of the one or more performance impacting factors associated with the one or more segments of the one or more routes is temporal (Kan mid-¶ [0069]: The step-154 determined driving segment start and end times may be retrieved from a database, calculated using heuristic rules (e.g., assume a 50 MPH speed); modified to account for daily traffic patterns, time of day, weather conditions, seasonal traffic patterns, and/or the like; or retrieved from another technology system; and/or the like).
Regarding Claim 7: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
wherein the ability of the one or more vehicle operators to reduce the impact of the extent of the one or more performance impacting factors is temporal (Kan ¶ [0055]: As described in more particular detail below, the systems and methods of the invention may make use of measured neurobehavioral performance levels which is typically only available when the subject is awake. Consequently, it may be desirable to describe the homeostatic process between successive periods that the test subject is awake…. ¶ [0057] Equation (6) applies to the circumstance where to occurs during a period when the test subject is awake, there is a single transition between awake and asleep at t1 (where t0 <t1 <t3), there is a single transition between asleep and awake at t2 (where t1 < t2 < t3), and then t3 occurs after the test subject is awake again. [Also see Fig. 5 and related text]).
Regarding Claim 8: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to determine the extent of the one or more performance impacting factors associated with the one or more segments of the one or more routes based on stored historical data related to the extent of the one or more performance impacting factors associated with the one or more segments of the one or more routes and further optionally based on one or more of the following:
[..];
one or more stored rules based on the stored historical data related to the extent of one or more performance impacting factors associated with the one or more segments of the one or more routes wherein the one or more stored rules are further optionally based on one or more self-learning algorithms (Kan mid-¶ [0007]: …receiving, at the processor, one or more driver hours-of-service rules, wherein the driver hours-of-service rules represent one or more constraints on a schedule of driving activities… generating, with the processor, one or more route plans based at least in part on the received trip data, each route plan comprising one or more route segments,
each route segment comprising at least in part a route segment start location and a route segment end location);
one or more stored models based on the stored historical data related to the extent of one or more performance impacting factors associated with the one or more segments of the one or more routes (Kan mid-¶ [0007]: …receiving, at the processor, a fatigue prediction model, the fatigue prediction model comprised to determine one or more fatigue levels associated with a driver based at least in part upon a sleep schedule for the driver; generating, with the processor, one or more route plans based at least in part on the received trip data, each route plan comprising one or more route segments, each route segment comprising at least in part a route segment start location and a route segment end location).
Regarding Claim 9: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to determine ability of the one or more vehicle operators to reduce the impact of the extent of the one or more performance impacting factors based on stored historical data related to the ability of the one or more vehicle operators to reduce impact of the extent of the one or more performance impacting factors and further optionally based on one or more of the following:
[..];
one or more stored rules based on the stored historical data related to the ability of the one or more vehicle operators to reduce impact of the extent of the one or more performance impacting factors wherein the one or more stored rules are further optionally based on one or more self- learning algorithms; one or more stored models based on the stored historical data related to the ability of the one or more vehicle operators to reduce impact of the extent of the one or more performance impacting factors. (See Kan Table 2: Heuristic Rules for Sleep Prediction. Mid-¶ [0060]: In particular (non-limiting) embodiments, the step-104 received sleep prediction model comprises a set of heuristic rules for estimating sleep intervals based upon work and/or activity schedules. Such heuristic rules (or "rules of thumb") may comprise best estimates of a person's sleep activity based upon observed patterns in either the individual him- or herself or in others similarly situated (e.g., shift workers, over-the-road drivers, pilots and flight attendants, emergency responders, medical professionals, etc.). In particular (non-limiting) embodiments, the
step-104 received sleep prediction model may comprise the following exemplary and non-limiting set of heuristic rules).
Regarding Claim 10: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to determine the anticipated physical and mental health of the one or more vehicle operators while operating the one or more vehicles based on stored historical data related to the physical and mental health of the one or more vehicle operators while operating the one or more vehicles and further optionally based on one or more of the following:
[..];
one or more stored rules based on the stored historical data related to the physical and mental health of the one or more vehicle operators while operating the one or more vehicles wherein the one or more stored rules are further optionally based on one or more self-learning algorithms; one or more stored models based on the stored historical data related to the physical and mental health of the one or more vehicle operators while operating the one or more vehicles (See Kan Table 2: Heuristic Rules for Sleep Prediction. Mid-¶ [0060]: In particular (non-limiting) embodiments, the step-104 received sleep prediction model comprises a set of heuristic rules for estimating sleep intervals based upon work and/or activity schedules. Such heuristic rules (or "rules of thumb") may comprise best estimates of a person's sleep activity based upon observed patterns in either the individual him- or herself or in others similarly situated (e.g., shift workers, over-the-road drivers, pilots and flight attendants, emergency responders, medical professionals, etc.). In particular (non-limiting) embodiments, the step-104 received sleep prediction model may comprise the following exemplary and non-limiting set of heuristic rules);
[..];
one or more inputs provided without one or more graphical user interfaces (Kan ¶ [0060]: …Method 100 may then proceed to step 104 wherein a sleep prediction model is received at the processor in accordance with particular embodiments. A step-104 received sleep prediction model comprises any set of computational tools that can estimate a person's anticipated sleep schedule ( or, conversely, their sleep history if applied retroactively) based upon any one or more inputs including, without limitation, work history, work schedule, activity history, activity schedule, fatigue level, and/or the like).
Regarding Claim 11: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to check that the assigned one or more segments of the one or more routes and the one or more vehicles to the one or more vehicle operators for the one or more trips comply with the one or more of the following by using one or more stored rules wherein the one or more stored rules are further optionally based on one or more self-learning algorithms:
occupational health and safety related rules; occupational health and safety related laws; occupational health and safety related regulations (Kan mid-¶ [0070]: By way of non-limiting example, if the updated elapsed travel time exceeds 8 hours without at least a 30 min break having already elapsed, this fact is noted in the step-156 rules check…. Based upon the result of this comparison, one or more actions may then be taken in step 157 with respect to the step-151 initialized trip schedule [EN: including assigned segments/routes etc.] under analysis: a) no stop is scheduled at the next node (because, e.g., no rules are violated by the elapsed time counter when updated in step 155), b) a short wait interval is suggested, c) a longer break ( e.g., a 30 required rest, a 2 hour meal or other substantial break, or an 8 to 12 hour rest period) may be inserted, d) a prior step-157 chosen no-stop/wait/break decision from an earlier iteration of method 150 may need to be modified and the subsequent route reassessed, and/or e) the step-151 initialized trip schedule under analysis needs to be discarded (because, e.g., there is no way to comply with the legal and/or regulatory rules or meet the business objectives such as on-time arrival, etc.). [Also see Table 1: Exemplary Hours-of-service Rules and related text]).
Regarding Claim 12: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to receive and store data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed (Kan ¶ [0099]: Method 100 may then proceed to optional step 117 wherein the trip schedule may updated with real-time information based upon a driver's actual progress in driving a trip in accordance with the step-116 provided trip schedule, according to particular embodiments).
Regarding Claim 13: Kan / Meroux teaches all the limitations of claim 12 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to check that the received and stored data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed, comply with the one or more of the following by using one or more stored rules wherein the one or more stored rules are further optionally based on one or more self-learning algorithms:
occupational health and safety related rules; occupational health and safety related laws; occupational health and safety related regulations (Kan ¶ [0070]: The elapsed time counter [EN: partially completed] is then additively updated with an estimated travel time for the driving segment comprising the difference between the step-154 determined driving segment end time and the step-154 determined driving segment start time. In particular embodiments, additional counters (e.g., time since last sleep, length of workday, length of workweek, time or distance since last fuel stop, etc.) (not shown) may be updated as well and used to determine legal and/or regulatory compliance along with other optimization factors (e.g., fuel efficiency, etc.). Method 150 may then proceed to step 156, in which the elapsed time is then checked against the HOS rules (or their equivalent). By way of non-limiting example, if the updated elapsed travel time exceeds 8 hours without at least a 30 min break having already elapsed, this fact is noted in the step-156 rules check. Additionally, if the updated elapsed time counter places the workday in excess of 14 hours, for example, this fact is noted, etc. The elapsed time is compared for compliance with workday and workweek length, break lengths, time since last rest, etc.).
Regarding Claim 14: Kan / Meroux teaches all the limitations of claim 12 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to auto-adjust the received and stored data related to the one or more assigned trips that the one or more vehicle operators have partially or fully completed data (Kan ¶ [0071] Method 150 may then proceed to step 158, wherein diving segment start and end times are updated for all subsequent segments in the step-151 selected trip schedule based upon the outcome of step 157. A check is made in step 159 to determine if estimated end times for each of the driving segments leading to the end waypoint and all time critical intermediate waypoints are satisfactory for business or other purposes. If not, the route is modified [EN: auto-adjusted] again-e.g., by redirecting method execution flow (shown in dashed lines) back to step 157 to choose another no-stop/wait/break/modify/discard decision in accordance with options a) through e) discussed therewith. If no additional processing in step 157 would make the proposed trip schedule both suitable for business purposes and compliant for legal and/or regulatory purposes, then the trip schedule is discarded, per option e) of step 157).
Regarding Claim 17: Kan / Meroux teaches all the limitations of claim 12 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to generate one or more of the following related to the received and stored trips data:
one or more insights; one or more recommendations (Kan ¶ [0006]: …the presently disclosed invention comprises one or more route planning algorithms that generate a trip schedule for a driver that will optimize for compliance with hours-of-service requirements, waypoint requirements, and other driving factors such as fatigue, driving conditions, delivery efficiency, fuel economy, other economic factors, and/or the like. According to particular embodiments, the provided trip schedule may include recommendations for waypoint locations, road segments, times at which to drive or go off-duty, and may include reports [EN: insights] about risks such as fatigue level or driving conditions);
one or more predictions (Kan ¶ [0007]: …applying the received sleep prediction model, at least in part, to the schedule of driving activities specified in a generated trip schedule; determining, with the processor, one or more fatigue levels associated with at least one generated trip schedule, wherein the fatigue levels are based upon applying the received fatigue prediction model to at least one of the one or more determined sleep schedules determined from the at least one generated trip schedule).
Regarding Claim 20: Claim 20 is the non-transitory computer readable storage medium claim corresponding to the computer-implemented method of claim 19 rejected above. Therefore, the rejection of claim 19 applies accordingly. Kan also teaches the additional elements claim 20 not recited in claim 19, the non-transitory computer readable storage medium with an executable program stored thereon, wherein the executable program is configured to instruct one or more processors to computer-implemented method (Kan ¶ [0033, 0101]).
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Claims 2-3, 15-16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over:
Kan / Meroux in further view of
Moore-Ede US 20060200008 A1, hereinafter Moore-Ede. As per,
Regarding Claim 2: Kan / Meroux teaches all the limitations of claim 1 above.
Kan further teaches:
further comprising one or more computer-executable instructions configured to determine the anticipated physical and mental health based on one or more stored weight [..] (Kan Claim 21: …the received optimization data comprises one or more of: a set of weighting criteria and a weighting function, and wherein selecting one or more optimal route plans from the one or more selected compliant route plans comprises applying the one or more of: a set of weighting criteria or a weighting function to the one or more of: fuel costs, toll costs, fatigue, probability of on-time pickup and delivery, probability of finding parking spots when arriving at truck stop) and one or more of the following:
historical data related to slept hours associated with the one or more vehicle operators (Kan ¶ [0060]: Method 100 may then proceed to step 104 wherein a sleep prediction model is received at the processor in accordance with particular embodiments. A step-104 received sleep prediction model comprises any set of computational tools that can estimate a person's anticipated sleep schedule (or, conversely, their sleep history if applied retroactively) based upon any one or more inputs including, without limitation, work history, work schedule, activity history, activity schedule, fatigue level, and/or the like);
[..].
Although Kan teaches determining anticipated physical and mental health based on weights, Kan does not specifically teach the weights being associated with a profile.
However, Moore-Ede in analogous art of optimizing travel routes for vehicle operators teaches or suggests:
[..] determine the anticipated physical and mental health based on one or more [..] profiles (Moore-Ede ¶ [0368]: It should be recognized that exact ordinate scale of probability rates (DOT recordables/per year/per driver) is not generalizable across trucking fleets. The scale for any given trucking operation will depend on the other intrinsic risks in that operation. Hence the scale on the ordinate of FIGS. 8 and 9 will vary depending on the other factors (e.g. nature of loads, age of vehicles, road conditions, training of drivers, etc.) which contribute to the overall risk of the particular operation. The factors are addressed by also creating a driver profile score and calculating overall risk by combining the fatigue risk score with the drive profile score).
Moore-Ede, Meroux and Kan are found as analogous art of optimizing travel routes for vehicle operators. It would have been obvious to one skilled in the art, before the effective filing date of the invention, to have modified Kan’s optimized routing and tracking system and method to have included Moore-Ede’s teachings around optimizing trips using profile scores for vehicle operators. The benefit of these additional features would have m