DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 12 use the terms “current predicted efficiency value” and “future predicted efficiency value” however the terms are not clearly distinct. The definition of “predict” is “to declare or tell in advance; prophesy; foretell.” The use of “predicted” in both terms suggests that they are both for a future efficiency value. In other words, they both appear to be future efficiency values. For the purposes of compact prosecution, the “current predicted efficiency value” is a prediction made about the efficiency value using current operation of the vehicle whereas the “future predicted efficiency value” is a prediction made in the efficiency value using an updated operation of the vehicle.
Claims 2-11 and 13-20 depend upon claims 1 or 12 and therefore inherit the above rejections of claims 1 and 12.
The term “harsh vehicle acceleration” in claims 4 and 15 is a relative term which renders the claim indefinite. The term “harsh vehicle acceleration” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. There is no way to determine when acceleration becomes harsh acceleration.
The term “sudden vehicle braking” in claims 4 and 15 is a relative term which renders the claim indefinite. The term “sudden vehicle braking” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. There is no way to determine when braking becomes sudden braking.
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.
Alice/Mayo Framework Step 1:
Claims 1-11 recite a series of steps and therefore recite a process.
Claims 12-20 recite a combination of devices and therefore recite a machine.
Alice/Mayo Framework Step 2A – Prong 1:
Claims 1 and 12, as a whole, are directed to the abstract idea of receiving vehicle operations data, determining vehicle efficiency, comparing vehicle efficiencies, and improving a vehicles efficiency based on the comparison, which is a mathematical concept, method of organizing human activity, and mental process. The claims recite a mathematical concept because the identified idea is a mathematical calculation by reciting predicting efficiency values. See MPEP 2106.04(a)(2)(I)(C). The claims recite a method of organizing human activity because the identified idea is managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) by reciting instructions for operating a vehicle more efficiently. See MPEP 2106.04(a)(2)(II)(C). The claims recite a mental process because the identified idea contains limitations that can practically be performed in the human mind (including an observation, evaluation, judgement, or opinion) by reciting determining if a future efficiency is less than a current efficiency and comparing efficiencies. See MPEP 2106.04(a)(2)(III). The mathematical concept, method of organizing human activity, and mental process of “receiving vehicle operations data, determining vehicle efficiency, comparing vehicle efficiencies, and improving a vehicles efficiency based on the comparison,” is recited by claiming the following limitations: receiving current vehicle data, retrieving external data, selecting current operation data, generating current predicted efficiency, generating future predicted efficiency, determining if a first vehicle efficiency is less than a second vehicle efficiency, comparing operational characteristics, and providing a message to improve efficiency. The mere nominal recitation of a network, a vehicle fleet, a database, a first machine learning algorithm, and a second machine learning algorithm, a data input element, an artificial neural network engine, a data processing device, and a data output element does not take the claim of the mathematical concept, method of organizing human activity, and mental process grouping. Thus, the claim recites an abstract idea.
With regards to Claims 3, 5, 7-8, 14, and 17-19, the claims further recite the above-identified judicial exception (the abstract idea) by reciting the following limitations: modifying a driving characteristic, adjusting a travel route, identifying maintenance, adjusting a speed, generating a real time adjustability factor, and evaluating a confidence interval.
Alice/Mayo Framework Step 2A – Prong 2:
Claims 1 and 12 recite the additional elements: a network, a vehicle fleet, a database, a first machine learning algorithm, a second machine learning algorithm, a data input element, an artificial neural network engine, a data processing device, and a data output element. These a network, a database, a first machine learning algorithm, and a second machine learning algorithm, a data input element, an artificial neural network engine, a data processing device, and a data output element. limitations are no more than mere instructions to apply the exception using a generic computer component. The data input element step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The vehicle fleet limits the field of use by generally linking the identified abstract idea to the vehicle management field. Taken individually these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Considering the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception does not amount to a practical application of the abstract idea. The claim as a whole does not improve the functioning of a computer or improve other technology or improve a technical field. The claim as a whole is not implemented with a particular machine. The claim as a whole does not effect a transformation of a particular article to a different state. The claim as a whole is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The claim as a whole merely describes how to generally “apply” the concept of optimizing vehicle performance in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing vehicle operations process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim is directed to the abstract idea.
Alice/Mayo Framework Step 2B:
Claims 1 and 12 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite a generic computer performing generic computer function by reciting a network, a database, a data input element, a data processing device, and a data output element. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1341 (describing a “processor” as a generic computer component); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347–48 (discussing the same with respect to “data” and “memory”). The claims recite the following computer functions recognized by the courts as generic computer functions by reciting receiving and transmitting information (See MPEP 2106.05(d)(II) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec; TLI Communications LLC; OIP Techs.; buySAFE, Inc.), processing information (See MPEP 2106.05(d)(II) performing repetitive calculations, Flook; Bancorp Services), presenting information (See MPEP 2106.05(d)(II), MPEP 2106.05(g) presenting offers gathering statistics, OIP Technologies), and retrieving information (See MPEP 2106.05(d)(II) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Technologies). The specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a network (Specification [0035]), a vehicle fleet (Specification [0032]), a database (Specification [0034], [0063], [0074]), a first machine learning algorithm (Specification [0037]), and a second machine learning algorithm (Specification [0037]), a data input element (Specification [0040], [0062]), an artificial neural network engine (Specification [0011], [0029], [0036]), a data processing device (Specification [0025]), and a data output element (Specification [0040]). See MPEP 2106.05(d)(I)(2). The claims add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a network, a database, a first machine learning algorithm, a second machine learning algorithm, a data input element, an artificial neural network engine, a data processing device, and a data output element. See MPEP 2106.05(f). The claims recite insignificant extrasolution activity (i.e. mere data gathering, selecting a particular data source or type of data to be manipulated, or an insignificant application) by reciting a data input element. See MPEP 2106.05(g). The claims limit the field of use by reciting a vehicle fleet. See MPEP 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements alone, and in combination, that are sufficient to amount to significantly more than the recited judicial exception.
With regards to Claims 11, the additional elements do not amount to significantly more than the judicial exception. Regarding claims 11, the specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a set of sensors (Specification [0051]-[0052], [0054]). See MPEP 2106.05(d)(I)(2). Claims 11 add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a set of sensors. See MPEP 2106.05(f). Claims 11 recite insignificant extrasolution activity (i.e. mere data gathering, selecting a particular data source or type of data to be manipulated, or an insignificant application) by reciting a set of sensors. See MPEP 2106.05(g). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exception.
Remaining Claims:
With regards to Claims 2, 4, 6, 9-10, 13, 15-16, and 20, these claims merely add a degree of particularity to the limitations discussed above rather than adding additional elements capable of transforming the nature of the claimed subject matter. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements 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 conventional computer implementation. Therefore, the claims as a whole do not amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lane et al. (U.S. P.G. Pub. 2023/0194281 A1), hereinafter Lane.
Claim 1.
Lane discloses a method comprising:
receiving a set of current data values for a group of vehicles over a network, the group of vehicles operating in unison as a vehicle fleet, each of the current data values being generated by at least one of the vehicles in the group of vehicles (Lane [0023], [0028], [0031], [0032], [0043], [0046], [0050], [0067], [0070], [0077], [0079], [0081], [0082], [0083], [0098] onboard sensors; [0070] polling sensors using a communication network; [0017], [0023], [0025], [0096], [0098], [0101] fleet);
retrieving data external to the group of vehicles from at least one database (Lane [0031], [0033], [0041], [0046], [0069] weather service; [0032], [0049] age of the battery; [0036], [0071] store parameters for type, make, and model of machine or equipment, age and condition of each individual machine);
processing the set of current data values and the received external data for the group of vehicles using at least two machine learning algorithms to identify a set of selected data values, the set of selected data values being selected based on current operation of the group of vehicles (Lane [0024], [0027], [0031], [0032], [0034], [0038]-[0039], [0043], [0046], [0053] data inputs);
processing the set of selected data values, using a first machine learning algorithm selected from the at least two machine learning algorithms, to generate a current predicted efficiency value for each vehicle in the group of vehicles (Lane [0017], [0023], [0024], [0087] machine learning for energy consumption; [0021], [0088] machine learning for state of health of a battery; [0024], [0026], [0064], [0067], [0084], [0093], [0098], [0101] actual energy consumption);
processing the current predicted efficiency value for the group of vehicles, using a second machine learning algorithm, to generate at least one future predicted efficiency value for each vehicle in the group of vehicles at a future point in time (Lane [0026], [0086] machine learning for predicting energy requirements for upcoming travel routes; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters);
determining if one of the current predicted efficiency value and the at least one future predicted efficiency value of a first vehicle in the group of vehicles is less than one of the current predicted efficiency value and the corresponding at least one future predicted efficiency value for a second vehicle in the group of vehicles (Lane [0029], [0040], [0055], [0073] select optimal operational parameters; [0033], [0042], [0088], [0094], [0101] predicting maintenance; [0095], [0101] recommend a change in travel route; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters; [0028], [0084], [0101] comparison to historical segment performance);
comparing at least one operational characteristic of the first vehicle and the second vehicle if it is determined that one of the current predicted efficiency value and the at least one future predicted efficiency value of the first vehicle is less than one of the current predicted efficiency value and the corresponding at least one future predicted efficiency value of the second vehicle (Lane Fig. 1; [0029], [0040], [0055], [0073] select optimal operational parameters; [0033], [0042], [0088], [0094], [0101] predicting maintenance; [0095], [0101] recommend a change in travel route; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters; [0028], [0084], [0101] comparison to historical segment performance); and
providing a message in order to improve efficiency for the first vehicle in the group of vehicles based on the comparison (Lane [0040], [0073] a display may be provided to an operator with an optimal speed, or commands; [0033], [0094], [0095], [0099], [0100] instruct an operator or autonomous control system to relace or perform maintenance on the batteries).
Claim 2.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the current predicted efficiency is at least one of current predicted fuel efficiency and current predicted battery efficiency and the at least one future predicted efficiency value is at least one of future predicted fuel efficiency and future predicted battery efficiency (Lane [0004], [0025], [0037], [0048], [0076] energy (e.g., fuel or electric charge); [0017], [0023], [0024], [0087] machine learning for energy consumption; [0021], [0088] machine learning for state of health of a battery; [0026], [0086] machine learning for predicting energy requirements for upcoming travel routes; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters).
Claim 3.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the efficiency improvement includes at least one of modifying a driving characteristic of the first vehicle, adjusting a travel route of the first vehicle, and identifying maintenance for the first vehicle (Lane [0029], [0040], [0055], [0073] select optimal operational parameters; [0033], [0042], [0088], [0094], [0101] predicting maintenance; [0095], [0101] recommend a change in travel route).
Claim 4.
Lane discloses all the elements of claim 3, as shown above. Additionally, Lane discloses:
wherein the at least one operational characteristic of the first vehicle is at least one of harsh vehicle acceleration, vehicle operation idle time, and sudden vehicle braking (Lane [0027] accelerate or decelerate (e.g., override the system) for emergency situations).
Claim 5.
Lane discloses all the elements of claim 3, as shown above. Additionally, Lane discloses:
wherein the modifying of the driving of the first vehicle includes adjusting a speed of the first vehicle automatically by sending control instructions to the first vehicle over a network (Lane [0027], [0028], [0040] optimal speed).
Claim 6.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein providing the message includes providing the message to an operator of the at least one vehicle while driving the first vehicle (Lane [0040], [0073] a display may be provided to an operator with an optimal speed, or commands; [0033], [0094], [0095], [0099], [0100] instruct an operator or autonomous control system to relace or perform maintenance on the batteries).
Claim 7.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein each one of the selected set of data values are identified from the current set of data values based on a real time adjustability factor generated by the first machine learning algorithm selected from the at least two machine learning algorithms (Lane [0024] adjust the speed and/or other operational parameters; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters; [0034], [0053] minimum and maximum speed).
Claim 8.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the first machine learning algorithm is selected from the at least two machine learning algorithms by evaluating a confidence interval for each of the at least two machine learning algorithms and selecting a machine learning algorithm having a confidence interval greater than a predetermined threshold confidence interval as the first machine learning algorithm (Lane [0084] calibration engine; [0086] modifying a learning parameter to decrease the differences responsive to the differences being greater than threshold differences; [0093] compare analyzed data with predetermined threshold values for various parameters).
Claim 9.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the first machine learning algorithm is not the same as the second machine learning algorithm (Lane [0017], [0023], [0024], [0087] machine learning for energy consumption; [0021], [0088] machine learning for state of health of a battery; [0026], [0086] machine learning for predicting energy requirements for upcoming travel routes; [0029], [0085] machine learning control for optimal speeds, gear rations, and other operational parameters; [0086], [0089] neural network, support vector machine, or Markov decision process engine; [0086], [0089] support vector machine; [0086], [0089] Markov decision process engine).
Claim 10.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the data external to the group of vehicles includes at least one of geographic location for a vehicle in the group of vehicles, vehicle type identification for a vehicle in the group of vehicles, age of a vehicle, and environmental conditions around a vehicle in the group of vehicles (Lane [0031], [0033], [0041], [0046], [0069] weather service; [0032], [0049] age of the battery; [0036], [0071] store parameters for type, make, and model of machine or equipment, age and condition of each individual machine).
Claim 11.
Lane discloses all the elements of claim 1, as shown above. Additionally, Lane discloses:
wherein the current set of data values are provided by a set of sensors included in each one of the vehicles in the group of vehicles (Lane [0023], [0028], [0031], [0032], [0043], [0046], [0050], [0067], [0070], [0077], [0079], [0081], [0082], [0083], [0098] onboard sensors).
Claim 12.
Lane discloses all the elements of claim 12 as shown above in claim 1.
Claim 13.
Lane discloses all the elements of claim 13 as shown above in claim 2.
Claim 14.
Lane discloses all the elements of claim 14 as shown above in claim 3.
Claim 15.
Lane discloses all the elements of claim 15 as shown above in claim 4.
Claim 16.
Lane discloses all the elements of claim 16 as shown above in claim 6.
Claim 17.
Lane discloses all the elements of claim 17 as shown above in claim 5.
Claim 18.
Lane discloses all the elements of claim 18 as shown above in claim 7.
Claim 19.
Lane discloses all the elements of claim 19 as shown above in claim 8.
Claim 20.
Lane discloses all the elements of claim 20 as shown above in claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT M TUNGATE whose telephone number is (571)431-0763. The examiner can normally be reached Monday - Friday, 9:00 - 4:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SCOTT M TUNGATE/Primary Examiner, Art Unit 3628