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 .
Application Status
Claims 1-20 are pending and have been examined in this application.
This communication is the first action on the merits.
An information disclosure statement (IDS) has been filed on 10 July 2024 and reviewed by the Examiner.
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.
101 Analysis – Step 1
Claim 1 is directed to a system, claim 11 is directed to a method and claim 19 is directed to a system. Therefore, claims 1, 11 and 19 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 11 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claims 1 and 19 are rejected for the same reasons as the representative claim 11 as discussed here. Claim 11 recites:
A method comprising: receiving vehicle trace data from connected vehicles traversing an environment; applying a Simultaneous Localization and Mapping (SLAM) algorithm to the vehicle trace data to generate a SLAM representation of the environment; using a machine learning model to quantify accuracy of the SLAM representation based on values for pre-selected features of the SLAM representation; generating a digital representation for the quantified accuracy of the SLAM representation; and providing, via a graphical user interface (GUI) of a user device, the digital representation for the quantified accuracy of the SLAM representation
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, quantifying accuracy of [a] representation ... in the context of this claim encompasses a person looking at data collected (received, detected, generated, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method comprising: receiving vehicle trace data from connected vehicles traversing an environment; applying a Simultaneous Localization and Mapping (SLAM) algorithm to the vehicle trace data to generate a SLAM representation of the environment; using a machine learning model to quantify accuracy of the SLAM representation based on values for pre-selected features of the SLAM representation; generating a digital representation for the quantified accuracy of the SLAM representation; and providing, via a graphical user interface (GUI) of a user device, the digital representation for the quantified accuracy of the SLAM representation
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations underlined above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the receiving step(s) is recited at a high level of generality (i.e. as a general means of receiving information for use in the next steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The applying SLAM to generate [a] representation, generating a digital representation and providing the digital representation steps are also recited at a high level of generality (i.e. as a general means of creating a representation from some of the previous steps and outputting it), and amounts to mere post solution action, which is a form of insignificant extra-solution activity. Lastly, claims 1, 11 and 19 further recite the “A system comprising: one or more processing resources; non-transitory computer-readable medium, coupled to the one or more processing resources, comprising stored instructions that when executed by the one or more processing resources, cause the system to: ...; use a machine learning model to ...” (claim 1); “... using a machine learning model to ..., via a graphical user interface (GUI) of a user device ...” (claim 11) and “A system comprising: a graphical user interface (GUI); one or more processing resources coupled to the GUI; and non-transitory computer-readable medium, coupled to the one or more processing resources, comprising stored instructions that when executed by the one or more processing resources, cause the system to: ...; use a machine learning model to ..., via the GUI ...” (claim 19) which merely describes how to generally “apply” the otherwise mental judgements and/or additional limitations in a generic or general purpose vehicle control environment. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). The device(s) and processor(s) are recited at a high level of generality and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 11 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of receiving data are well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitations of applying an algorithm to generate [a] representation, generating a digital representation and providing the digital representation are well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that similar mere performances are a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent claims 2-10, 12-18 and 20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10, 12-18 and 20 are not patent eligible under the same rationale as provided for in the rejection of claim 11.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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 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.
Claims 1, 4, 8-11, 14, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Baus (US20240175711A1) in view of Wang (US20240328816A1) in further view of Nakano (US20050096839A1).
Regarding claim 11, Baus discloses a method (see at least claim 1) comprising: receiving vehicle trace data from connected vehicles traversing an environment (see at least [0012] and [0053]); applying a Simultaneous Localization and Mapping (SLAM) algorithm to the vehicle trace data to generate a SLAM representation of the environment (see at least [0012] and [0054]); the importance of accuracy of the SLAM representation (see at least [0015] and [0044]); and the SLAM representation being a map representation (see at least [0012] and [0054]).
Baus fails to disclose using a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation; generating a digital representation for the quantified accuracy of the map representation; and providing, via a graphical user interface (GUI) of a user device, the digital representation for the quantified accuracy of the map representation.
Wang teaches using a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation (see at least [0020], [0043]-[0045] and [0050]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus to incorporate the teachings of Wang which teaches using a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation since they are both directed to map representations (maps) and incorporation of the teachings of Wang would increase utility and accuracy of the overall system by quantifying the actual precision/accuracy of the SLAM/map representation for future analysis.
Nakano teaches generating a digital representation for the quantified accuracy of the map representation; and providing, via a graphical user interface (GUI) of a user device, the digital representation for the quantified accuracy of the map representation (see at least Figure 8, [0092] and [0153]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang to incorporate the teachings of Nakano which teaches generating a digital representation for the quantified accuracy of the map representation; and providing, via a graphical user interface (GUI) of a user device, the digital representation for the quantified accuracy of the map representation since they are directed to map representations (maps) and incorporation of the teachings of Nakano would increase user comfort and reliability of the overall system (see at least Nakano [0260]).
Regarding claim 14, Baus as modified by Wang and Nakano discloses wherein the SLAM representation comprises a geometric map (see at least Baus Figure 1, [0012], [0037] and [0054]).
Regarding claim 18, Baus as modified by Wang and Nakano discloses wherein the vehicle trace data comprises at least one of: data related to three-dimensional (3D) trajectories of the connected vehicles as the connected vehicles traverse the environment; or data related to landmarks observed by sensors of the connected vehicles as the connected vehicles traverse the environment (see at least Baus Figure 1, [0012], [0038], [0042] and [0053]).
Regarding claim 1, Baus discloses a system comprising: one or more processing resources; non-transitory computer-readable medium, coupled to the one or more processing resources, comprising stored instructions that when executed by the one or more processing resources, cause the system to (see at least claim 10). The rest of claim 1 is commensurate in scope with claim 11. See above for rejection of claim 11.
Regarding claim 4, claim 4 is commensurate in scope with claim 14. See above for rejection of claim 14.
Regarding claims 8 and 9, claims 8 and 9 are commensurate in scope with claim 11. See above for rejection of claim 11.
Regarding claim 10, claim 10 is commensurate in scope with claim 18. See above for rejection of claim 18.
Regarding claim 19, Baus discloses a system (see at least claim 10) comprising: one or more processing resources (see at least claim 10); and non-transitory computer-readable medium, coupled to the one or more processing resources, comprising stored instructions that when executed by the one or more processing resources, cause the system to (see at least claim 10): receive vehicle trace data from connected vehicles traversing an environment (see at least [0012] and [0053]); apply a Simultaneous Localization and Mapping (SLAM) algorithm to the vehicle trace data to generate a SLAM representation of the environment (see at least [0012] and [0054]); the importance of accuracy of the SLAM representation (see at least [0015] and [0044]); and the SLAM representation being a map representation (see at least [0012] and [0054]).
Baus fails to disclose the system comprising: a graphical user interface (GUI); the one or more processing resources coupled to the GUI; use a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation; generate a digital representation for the quantified accuracy of the map representation; and provide, via the GUI, the digital representation for the quantified accuracy of the map representation.
Wang teaches using a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation (see at least [0020], [0043]-[0045] and [0050]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus to incorporate the teachings of Wang which teaches using a machine learning model to quantify accuracy of the map representation based on values for pre-selected features of the map representation since they are both directed to map representations (maps) and incorporation of the teachings of Wang would increase utility and accuracy of the overall system by quantifying the actual precision/accuracy of the SLAM/map representation for future analysis.
Nakano teaches the system comprising: a graphical user interface (GUI); the one or more processing resources coupled to the GUI; generating a digital representation for the quantified accuracy of the map representation; and providing, via the GUI, the digital representation for the quantified accuracy of the map representation (see at least Figure 1, Figure 8, [0055], [0092] and [0153]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang to incorporate the teachings of Nakano which teaches the system comprising: a graphical user interface (GUI); the one or more processing resources coupled to the GUI; generating a digital representation for the quantified accuracy of the map representation; and providing, via the GUI, the digital representation for the quantified accuracy of the map representation since they are directed to map representations (maps) and incorporation of the teachings of Nakano would increase user comfort and reliability of the overall system (see at least Nakano [0260]).
Claims 2-3 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Baus (US20240175711A1) in view of Wang (US20240328816A1) in further view of Nakano (US20050096839A1) in yet further view of Watanabe (US20220099458A1).
Regarding claim 12, Baus as modified by Wang and Nakano does not explicitly disclose wherein the pre-selected features of the SLAM representation comprise: distance between landmarks in the SLAM representation; and node degrees for the landmarks in the SLAM representation. However, Watanabe teaches wherein the pre-selected features of the SLAM representation comprise: distance between landmarks in the SLAM representation; and node degrees for the landmarks in the SLAM representation (see at least [0019], [0027], [0033] and [0062]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Watanabe which teaches wherein the pre-selected features of the SLAM representation comprise: distance between landmarks in the SLAM representation; and node degrees for the landmarks in the SLAM representation since they are directed to map representations (maps) and incorporation of the teachings of Watanabe would increase accuracy of the overall system by incorporating various factors/features that are known to affect the accuracy of map representations.
Regarding claim 13, Baus as modified by Wang and Nakano does not explicitly disclose wherein a node degree for a respective landmark in the SLAM representation corresponds to a number of observations from different viewpoints the SLAM representation correlates with the respective landmark. However, Watanabe teaches wherein a node degree for a respective landmark in the SLAM representation corresponds to a number of observations from different viewpoints the SLAM representation correlates with the respective landmark (see at least [0019], [0027], [0033] and [0062]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Watanabe which teaches wherein a node degree for a respective landmark in the SLAM representation corresponds to a number of observations from different viewpoints the SLAM representation correlates with the respective landmark since they are directed to map representations (maps) and incorporation of the teachings of Watanabe would increase accuracy of the overall system by incorporating various factors/features that are known to affect the accuracy of map representations.
Regarding claims 2 and 3, claims 2 and 3 are commensurate in scope with claims 12 and 13, respectively. See above for rejection of claims 12 and 13.
Claims 5, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Baus (US20240175711A1) in view of Wang (US20240328816A1) in further view of Nakano (US20050096839A1) in yet further view of Miki (US20210368161A1).
Regarding claim 15, Baus as modified by Wang does not explicitly disclose wherein the digital representation for the quantified accuracy of the SLAM/map representation comprises a heat map superimposed on the SLAM/map representation.
Nakano teaches wherein the digital representation for the quantified accuracy of the map representation comprises a measurement superimposed on the map representation (see at least Figure 8, [0092] and [0153]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang to incorporate the teachings of Nakano which teaches wherein the digital representation for the quantified accuracy of the map representation comprises a measurement superimposed on the map representation since they are directed to map representations (maps) and incorporation of the teachings of Nakano would increase user comfort and reliability of the overall system (see at least Nakano [0260]).
Miki teaches the measurement being represented as a heat map (see at least [0038], [0039] and [0095]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Miki which teaches the measurement being represented as a heat map since they are directed to map representations (maps) and incorporation of the teachings of Miki would increase user comfort and reliability of the overall system by incorporating another user friendly way to present the digital representation of the accuracy.
Regarding claim 5, claim 5 is commensurate in scope with claim 15. See above for rejection of claim 15.
Regarding claim 20, Baus as modified by Wang discloses wherein: the SLAM representation comprises a geometric map (see at least Baus Figure 1, [0012], [0037] and [0054]).
Baus as modified by Wang does not explicitly disclose wherein the digital representation for the quantified accuracy of the SLAM/map representation comprises a heat map superimposed on the geometric map.
Nakano teaches wherein the digital representation for the quantified accuracy of the map representation comprises a measurement superimposed on the geometric map (see at least Figure 8, [0092] and [0153]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang to incorporate the teachings of Nakano which teaches wherein the digital representation for the quantified accuracy of the map representation comprises a measurement superimposed on the geometric map since they are directed to map representations (maps) and incorporation of the teachings of Nakano would increase user comfort and reliability of the overall system (see at least Nakano [0260]).
Miki teaches the measurement being represented as a heat map (see at least [0038], [0039] and [0095]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Miki which teaches the measurement being represented as a heat map since they are directed to map representations (maps) and incorporation of the teachings of Miki would increase user comfort and reliability of the overall system by incorporating another user friendly way to present the digital representation of the accuracy.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Baus (US20240175711A1) in view of Wang (US20240328816A1) in further view of Nakano (US20050096839A1) in yet further view of Wheeler (US20200081134A1).
Regarding claim 16, Baus as modified by Wang and Nakano does not explicitly disclose wherein the machine learning model comprises a random forest model. However, Wheeler teaches wherein the machine learning model comprises a random forest model (see at least [0086]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Wheeler which teaches wherein the machine learning model comprises a random forest model since they are directed to map representations (maps) and incorporation of the teachings of Wheeler would introduce another machine learning model that could be useful for the overall system to increase accuracy.
Regarding claim 6, claim 6 is commensurate in scope with claim 16. See above for rejection of claim 16.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Baus (US20240175711A1) in view of Wang (US20240328816A1) in further view of Nakano (US20050096839A1) in yet further view of Garg (US20200160148A1).
Regarding claim 17, Baus as modified by Wang and Nakano discloses wherein the machine learning model is used to quantify accuracy for the SLAM/map representation related to traffic environments (see claim 11 above).
Baus as modified by Wang and Nakano does not explicitly disclose wherein the machine learning model uses a five-fold cross-validation method for individual regions of traffic environments. However, Garg teaches wherein the machine learning model uses a five-fold cross-validation method for individual regions of traffic environments (see at least [0026]). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, with a reasonable expectation of success, to have modified Baus as modified by Wang and Nakano to incorporate the teachings of Garg which teaches wherein the machine learning model uses a five-fold cross-validation method for individual regions of traffic environments since they are directed to traffic environments and incorporation of the teachings of Garg would increase accuracy of the overall system.
Regarding claim 7, claim 7 is commensurate in scope with claim 17. See above for rejection of claim 17.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAHAR MOTAZEDI whose telephone number is (571)272-0661. The examiner can normally be reached Monday-Friday 10a.m. - 6p.m..
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/SAHAR MOTAZEDI/Primary Examiner, Art Unit 3667
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