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 § 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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Each of Claims 1-12 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-12 recites at least one step or instruction for selecting an optimized speed profile minimizing energy consumption of a fuel rail, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG.
Selecting an optimized speed profile minimizing energy consumption of a fuel rail (involves managing interactions between people, namely, humans following rules, which is grouped as a certain method of organizing human activity under 2019 PEG and/or a judgement or evaluation, which is grouped as a mental process under 2019 PEG);
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Further, dependent Claims 2-9 and 12 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1, 10, and 11 (and their respective dependent Claims 2-9 and 12) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 1, 10, and 11), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: an optimization system are generically recited computer elements in independent Claims 1, 10, and 11 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1, 10, and 11 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., an optimization system as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1, 10, and 11 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1, 10, and 11 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-12 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: a generator module, an efficiency module, a consumption forecasting module, and a selector module.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the optimization system. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-12 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the systems and methods of Claims 1-12 are directed to applying an abstract idea (e.g., mental process or certain method of organizing human activity) on a general purpose computer without (i) improving the performance of the computer itself (as in McRO, Bascom and Enfish), or (ii) providing a technical solution to a problem in a technical field (as in DDR). In other words, none of Claims 1-12 provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1, 10, and 11 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. As such, the above-identified additional elements, when viewed as whole, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-12 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-12 amounts to significantly more than the abstract idea itself.
Accordingly, Claims 1-12 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas implemented on a generic computer in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al. and 2019 PEG.
Claim Rejections - 35 USC § 102
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.
Claims 1-6 and 9-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yazhemsky et al US 2021/0263527.
Regarding claims 1, 10, and 11 Yazhemsky et al discloses a rail vehicle (10) including: a plurality of components (14) comprising at least one of the following: traction motors (14a) of the rail vehicle (10); a braking system (14b) of the rail vehicle (10); converters (14c) of the rail vehicle (10); Heating, Ventilation and Air Conditioning, HVAC, (14d) of the rail vehicle (10) (see paragraph [0014]); and suspensions (14e) of the rail vehicle (10), and an optimization system (20) coupled to the components (14) and configured to receive input data and, based on the received input data, generate a plurality of candidate speed profiles and select, among the candidate speed profiles, an optimized speed profile for driving the rail vehicle(10), the optimized speed profile minimizing the energy consumption of the rail vehicle (10) (see paragraph [0049]),the input data comprising: kinematics data (D1) determined in real-time and indicative of real-time kinematics information of the rail vehicle (10) (see paragraph [0051]); track data (D2) indicative of a path to be travelled by the rail vehicle (10) (see paragraph [0056]); constraint data (D4) indicative of constraints for the functioning of the rail vehicle (10) (see paragraphs [0075] and [0060]), the constraint data (D4) comprising at least one among: a maximum speed of the rail vehicle (10); a maximum acceleration of the rail vehicle (10); and a timetable scheduling the travel of the rail vehicle (10); and component condition data (D3) comprising operating parameters determined in real-time and indicative of the real-time functioning of the components (14). See FIG. 4 and paragraphs [0014], [0028], [0044], [0049], [0051], [0056], [0066], [0074]-[0076], [0083], and [0098].
Regarding claim 2, Yazhemsky et al discloses wherein the optimization system (20) is further configured to, based on the received input data, generate a respective candidate configuration parameter set for each candidate speed profile and select, among the candidate configuration parameter sets, an optimized configuration parameter set indicative of control parameters for controlling the components (14), the optimized configuration parameter set contributing to the minimization of the energy consumption of the rail vehicle (10). See FIG. 4 and paragraphs [0074]-[0076] and [0083].
Regarding claim 3, Yazhemsky et al discloses a generator module (22) configured to receive the kinematics data (D1), the track data (D2) and the constraint data (D4) and, based on the kinematics data (D1), the track data (D2) and the constraint data (D4), generate said plurality of candidate speed profiles and said respective plurality of candidate configuration parameter sets, each candidate configuration parameter set being generated based on a respective candidate speed profile of the candidate speed profiles (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); an efficiency module (24) configured to receive the candidate speed profiles, the candidate configuration parameter sets and the component condition data (D3) and, based on the candidate speed profiles, the candidate configuration parameter sets and the component condition data (D3), generate a respective candidate component efficiency set for each candidate configuration parameter set, each candidate component efficiency set being indicative of the predicted working efficiencies of the components (14) when the components (14) are controlled according to the respective candidate configuration parameter set and the rail vehicle (10) is controlled according to the respective candidate speed profile (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); a consumption forecasting module (26) configured to receive the candidate speed profiles, the candidate configuration parameter sets, the candidate component efficiency sets and the track data (D2) and, based on the candidate speed profiles, the candidate configuration parameter sets, the candidate component efficiency sets and the track data (D2), generate a respective candidate consumption prediction for each candidate speed profile, each candidate consumption prediction being indicative of a predicted energy consumption of the rail vehicle (10) or of each component (14) (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); and a selector module (28) configured to receive the candidate speed profiles, the candidate configuration parameter sets, the candidate consumption predictions and the component condition data (D3) and, based on the candidate consumption predictions and the component condition data(D3), select as the optimized candidate speed profile and the optimized configuration parameter set respectively the candidate speed profile and the associated candidate configuration parameter set having the candidate consumption prediction that is indicative of the lowest predicted energy consumption of the rail vehicle (10). See FIG. 4 and paragraphs [0014], [0028], [0044], [0049], [0051], [0056], [0066], [0074]-[0076], [0083], and [0098].
Regarding claim 4, Yazhemsky et al discloses wherein the consumption forecasting module (26) is based on machine learning or artificial intelligence techniques and is trained based on a training dataset comprising energy consumption data indicative of historical data about the measured energy consumption of the rail vehicle (10). See paragraph [0028].
Regarding claim 5, Yazhemsky et al discloses wherein the efficiency module (24) is based on physical models of the components (14). See FIG. 4 and paragraphs [0074]-[0076] and [0083].
Regarding claim 6, Yazhemsky et al discloses wherein the control parameters of the optimized configuration parameter set are variable in time. See paragraph [0044].
Regarding claim 9, Yazhemsky et al discloses wherein the optimization system (20) further comprises a predictive maintenance module configured to acquire the component condition data (D3) and generate predictive maintenance information indicative of the timing for replacing or performing maintenance on each component (14). See paragraph [0048].
Regarding claim 12, Yazhemsky et al discloses generating, by the optimization system (20) and based on the received input data, a respective candidate configuration parameter set for each candidate speed profile (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); and selecting, by the optimization system (20) and among the candidate configuration parameter sets, an optimized configuration parameter set indicative of control parameters for controlling the components (14), the optimized configuration parameter set contributing to the minimization of the energy consumption of the rail vehicle (10),wherein the steps of generating the candidate speed profiles and the candidate configuration parameter sets comprise generating, by a generator module (22) of the optimization system (10), said plurality of candidate speed profiles and said respective plurality of candidate configuration parameter sets based on the kinematics data (D1), the track data (D2) and the constraint data (D4), each candidate configuration parameter set being generated based on a respective candidate speed profile of the candidate speed profiles, and wherein the steps of selecting the optimized speed profile and the optimized configuration parameter set comprise: based on the candidate speed profiles, the candidate configuration parameter sets and the component condition data (D3), generating, by an efficiency module (24) of the optimization system (20), a respective candidate component efficiency set for each candidate configuration parameter set, each candidate component efficiency set being indicative of the predicted working efficiencies of the components (14) when the components (14) are controlled according to the respective candidate configuration parameter set and the rail vehicle (10) is controlled according to the respective candidate speed profile (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); based on the candidate speed profiles, the candidate configuration parameter sets, the candidate component efficiency sets and the track data (D2), generating, by a consumption forecasting module (26) of the optimization system (20), a respective candidate consumption prediction for each candidate speed profile, each candidate consumption prediction being indicative of a predicted energy consumption of the rail vehicle (10) or of each component (14) (See FIG. 4 and paragraphs [0074]-[0076] and [0083]); and based on the candidate consumption predictions and the component condition data (D3),selecting, by a selector module (28) of the optimization system (20), as the optimized candidate speed profile and the optimized configuration parameter set respectively the candidate speed profile and the associated candidate configuration parameter set having the candidate consumption prediction that is indicative of the lowest predicted energy consumption of the rail vehicle (10). See FIG. 4 and paragraphs [0014], [0028], [0044], [0049], [0051], [0056], [0066], [0074]-[0076], [0083], and [0098].
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, 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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yazhemsky et al, as applied above, in view of Ashby US 2023/0127799.
Regarding claim 7, Yazhemsky et al fails to explicitly disclose, but Ashby discloses
wherein the input data further comprise environmental data (D5) indicative of features of an environment in which the rail vehicle (10) is present, the environmental data (D5) comprising at least one among: an external temperature; an external humidity; and a wind speed. See FIG. 1 and paragraphs [0015]-[0021].
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include the environmental date of Ashby in the system of Yazhemsky et al to improve performance of the engine.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH J DALLO whose telephone number is (313)446-4844. The examiner can normally be reached 7am-7pm ET M-Th.
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/JOSEPH J DALLO/Primary Examiner, Art Unit 3747
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