DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on November 14, 2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to a 35 USC 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed November 14, 2025 towards 35 USC 101 have been fully considered but they are not persuasive.
The Applicant states that the Claims “recite an improvement in autonomous machine technology by reciting changes made to the operations of autonomous or semi-autonomous machines according to the other elements recited in the claims.” The Examiner respectfully disagrees.
The currently amended claims added concepts dealing with correlating data (i.e. comparing retrieved data). Such concepts may be performed in the mind, and are thus abstract. Furthermore, the purported improvements just restate the abstract ideas discussed above.
As such, the 35 USC 101 rejection is maintained.
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 ide without significantly more.
Independent Claims 1, 8 and 16 recite abstract subject matter directed towards correlating data. Specifically the claims recite:
obtaining first data including one or more performance indicator values corresponding to performance of a system included in an autonomous or semi-autonomous machine - This is a data gathering limitations, which is considered as insignificant extra-solution activity as per MPEP 2106.05(g).
obtaining second data including one or more values of one or more operational domain parameters corresponding to the system - This is a data gathering limitations, which is considered as insignificant extra-solution activity as per MPEP 2106.05(g).
assembling a data structure based at least on the first data and the second data, the assembling of the data structure including aligning, based at least on time, the first data and the second data – Constructing a data structure based on received data is something that can be performed in the human mind, or by a human using a pen and paper.
determining one or more respective correlations between at least one value of at least one operational domain parameter of the one or more operational domain parameters and at least one performance indicator value of the one or more performance indicator values based at least on the assembled data structure – Determining how different parameters are related (correlated) to each other is something that can be performed in the human mind, or by a human using a pen and paper.
the determining of the one or more respective correlations is based at least on performance indicator values and values of the operational domain parameters that are time aligned in the data structure – This describes an additional element of the data structure and is not integrated into a practical application
the one or more respective correlations indicate at least one effect of the at least one operational domain parameter on the at least one performance indicator value – Identifying whether one parameter has an effect on another parameter is something that can be performed in the human mind.
modifying one or more autonomous or semi-autonomous planning, navigation, or control operations of the autonomous or semi-autonomous machine based at least on the one or more respective correlations – Modifying at least planning of a machine may be performed in the mind. For example, identifying information around a person’s environment and mentally figuring out what command to send to a machine is a processing that is performed in the mind, and thus abstract. Also, applying changes based on an abstract concept is considered abstract based at least on 2106.05(f) “Mere Instructions To Apply An Exception.”
Independent Claims 1, 8 and 16 include additional elements such as hardware elements such as processors. However, the provided hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of above described limitations) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As such, this judicial exception is not integrated into a practical application and the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using hardware to perform the above limitation to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As such, the claims are not patent eligible.
Dependent Claims 2-7, 9-15 and 17-20 further describe more details of the above identified mental processes and thus do not provide additional elements that would make them statutory under 35 USC 101.
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.
Claims 1-6, 8-12 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al (US Patent Application Publication 2024/0328822) in view of Ramamurthy et al (US Patent Application Publication 2022/0405619) and further in view of Hsu et al (US Patent Application Publication 2023/0106214).
Claims 1, 8 and 16: Fu discloses a method, a system and a processor comprising:
obtaining first data including one or more performance indicator values corresponding to performance of a system included in an autonomous or semi-autonomous machine [0056, 0058, 0073]. [See at least receiving data regarding various parameters (i.e. parameters of a system), Performance data is obtained is from at least an autonomous vehicle, i.e. autonomous machine.]
obtaining second data including one or more values of one or more operational domain parameters corresponding to the system [0056, 0073]. [See at least receiving data of the ODD.]
While Fu [0056, 0073] discloses receiving and storing data, Fu alone does not explicitly disclose assembling a data structure based at least on the first data and the second data, the assembling of the data structure including aligning, based at least on time, the first data and the second data.
However, Ramamurthy [0124, 0127] discloses assembling a journal data structure that stores data from different sources. The data is aligned at least based on timestamps.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Fu with Ramamurthy. One would have been motivated to do so in order to keep track of received data.
Fu as modified further discloses:
determining one or more respective correlations between at least one value of at least one operational domain parameter of the one or more operational domain parameters and at least one performance indicator value of the one or more performance indicator values based at least on the assembled data structure [0056]. [“The conditions/parameters may accordingly comprise availability of certain data… correlation between data originating different data sources…”]
modifying one or more autonomous or semi-autonomous planning, navigation, or control operations of the autonomous or semi-autonomous machine based at least on the one or more respective correlations [0056]. [See at least defining scenarios based at least on “correlation between data originating different data sources…”]
Fu alone does not explicitly disclose wherein: the determining of the one or more respective correlations is based at least on performance indicator values and values of the operational domain parameters that are time aligned in the data structure, and the one or more respective correlations indicate at least one effect of the at least one operational domain parameter on the at least one performance indicator value.
However, Hsu [0061-0062] discloses correlating operational parameters (i.e. streaming) with environmental parameters (i.e. performance indicators) based on a particular time period. Hsu [0061-0062] also discloses identifying which parameter is correlated to a performance and how it effects the performance.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Fu with Hsu. One would have been motivated to do so in order to identify which parameter may be causing issues for performance.
Claims 2 and 10: Fu as modified discloses the method and the system of Claims 1 and 8 above, and Ramamurthy, for the same reasons as above, further discloses wherein the second data is obtained from a plurality of sources and is preprocessed to improve uniformity between the second data obtained from the plurality of sources [0080].
Claim 3: Fu as modified discloses the method of Claim 1 above, and Ramamurthy, for the same reasons as above, further discloses wherein the aligning of the first data and of the second data includes associating first data subsets and second data subsets that correspond to same time frames [0145].
Claims 4 and 17: Fu as modified discloses the method and the processor of Claims 1 and 16 above, and Fu in view of Ramamurthy, for the same reasons as above, further disclose the first data includes a first plurality of timestamps respectively corresponding to the one or more performance indicator values [See Fu [0056, 0073] for receiving performance values]; the second data includes a second plurality of timestamps respectively corresponding to the one or more operational domain parameters [See Fu [0056, 0073] for receiving ODD.]; and the aligning of the first data and the second data is based at least on the first plurality of timestamps and the second plurality of timestamps [See Ramamurthy [0145].]
Claims 5, 13 and 18: Fu as modified discloses the method, the system and the processor of Claims 1, 8 and 16 above and Fu further discloses wherein the determining of the one or more respective correlations is based at least on distributions between the performance indicator values and the values of the operational domain parameters that are time aligned in the data structure [0099]. [See at least making “correlated observations” based at least on time (i.e. time aligned).]
Claims 6 and 19: Fu as modified discloses the method and the processor of Claims 1 and 16 above, but Fu alone does not explicitly disclose identifying that a first operational domain parameter affects a particular performance indicator more than a second operational domain parameter based at least on the one or more respective correlations.
However, Hsu [0062] discloses identifying which parameter is correlated to a performance and how it effects the performance.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Fu with Hsu. One would have been motivated to do so in order to identify which parameter may be causing issues for performance.
Claim 9: Fu as modified discloses the system of Claim 8, but Fu alone does not explicitly disclose determining a causal connection between at least one score of the one or more scores of the operational domain parameters and at least one of the one or more performance indicator values based at least on the first data and the second data.
However, Fu [0056, 0073] discusses various parameters, including operational domain parameters as well as performance parameters; Hsu [0042] discloses using scores for various parameters to determine performance values. Meaning that a particular score contains a casual connection with a performance indicator.
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Fu with Hsu. One would have been motivated to do so in order to identify which parameter may be causing issues for performance.
Claim 11: Fu as modified discloses the system of Claim 8 above, and Fu, further discloses wherein: the first data includes a first plurality of timestamps respectively corresponding to the one or more performance indicator values; and the second data includes a second plurality of timestamps respectively corresponding to the one or more operational domain parameters [0056, 0073].
Claim 12: Fu as modified discloses the system of Claim 8 above, and Ramamurthy, for the same reasons as above, further discloses wherein the determining of the one or more respective correlations is based at least on distributions between performance indicator values and operational domain parameters that are time aligned in the data structure [0145].
Claims 15 and 20: Fu as modified discloses the system and the processor of Claims 15 and 16 above, and Fu further discloses wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system for hosting one or more real-time streaming applications; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for performing one or more generative AI operations; a system implementing one or more large language models (LLMs); a system implementing one or more visual language models (VLMs); a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources [0047]. [See at least autonomous machine.]
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al (US Patent Application Publication 2024/0328822) in view of Ramamurthy et al (US Patent Application Publication 2022/0405619) further in view of Hsu et al (US Patent Application Publication 2023/0106214) and further in view of Wang et al (US Patent Application Publication 2022/0027366).
Claims 7 and 14: Fu as modified discloses the method and the system of Claims 1 and 8 above, but Fu alone does not explicitly disclose wherein the determining of the one or more respective correlations includes determining at least one degree of confidence for at least one of the one or more respective correlations.
However, Wang [0049] discloses calculating a degree of confidence to “characterize the level of correlation.”
As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to modify Fu with Wang. One would have been motivated to do so in order to identify a relation or correlation between entities or parameters.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhao et al (2016/0320768) describes at least correlating performance to parameters during a particular time period.
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/ALEX GOFMAN/Primary Examiner, Art Unit 2163