DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 have been presented for examination based on the application filed on 4/30/2026.
Claims 1-20 remain rejected under 35 U.S.C. 101.
Claims 1-20 remain rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 1-20 remain rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
This action is made Final.
Response to Arguments
(Argument 1) Applicant has argued in Remarks Pg.11-12:
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(Response 1) The rejection is made under mathematical concept (MPEP 2106.04(a)(2)(I)(C) Mathematical Calculation"...A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation....") and mental step ((MPEP 2106.04(a)(2)(III)(C)). The claim is related to data gathering related to autonomous vehicle while is it changing lane and then computing reconstructed model variables that lead to computation of utility score. The computation of utility score to pick the right data set (no methodology described, hence either ad hoc or expert knowledge) based on some ad hoc error limit is remains akin to alarm limit (utility score here) in In re Flook. Just because the data is related to autonomous driving does not improve on the functioning of the autonomous vehicle (MPEP 2106.05(a)) and is at best field of use (MPEP 2106.05(h), more like In re Flook than Diehr).
(Argument 2) Applicant has argued in Remarks Pg.12-13:
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(Response 2) The arguments that the claim is directed to computation of a datum (utility factor and/or other factors) and does not lead to control of autonomous vehicle. The goal here is to find the correct sub-operational data. The finding of optimal subset of operational data (let say optimal velocity, position, acceleration, steering) does not translate into how the control system is improved/algorithm that is actually improved and used in control of autonomous vehicle. Further the most important aspect, selection of optimal datasets (n vs n+1 data sets) and error limits that lead to optimal data set selection appears to be ad hoc/expert knowledge/information from manufacturer. So the claim at best is mathematical evaluation of given condition.
(Argument 3) Applicant has argued in Remarks Pg.13-14:
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(Response 3) The scores are associated to data, however nothing is done with the scores other than selecting optimal sub-set of operational data. This does not improve the technology of lane change in the autonomous vehicle as there is no nexus between the autonomous vehicle control and optimal sub-set of operational data.
(Argument 4) Applicant has argued in Remarks Pg.13-14:
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(Response 4) It is unclear what technical problem is solved by step of “identify, at the processor, an optimal subset of operating data of the total set of operating data descriptive of the cyber-physical system…”.
(Argument 5) Applicant has argued in Remarks Pg.16-20:
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(Response 5) Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant’s arguments are considered, however applicant has not addressed any of the rejections specifically and more importantly applicant has not mapped the limitations to the disclosure to show where that support is to perform the steps as claimed. MPEP 2161.01 states:
For instance, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1349-50, 94 USPQ2d at 1171 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention’s boundaries.") (citing Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1405-06); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed); Fiers v. Revel, 984 F.2d 1164, 1170, 25 USPQ2d 1601, 1606 (Fed. Cir. 1993) (rejecting the argument that "only similar language in the specification or original claims is necessary to satisfy the written description requirement").
Similar response is provided for arguments made against remarks for rejection under 35 USC 112(b). Further MPEP 714 states:
The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06.
The amendments are not supported by mapping from specification to show how the amended limitations are performed. Although arguments are considered, they are not supported by the specification. Examiner has reviewed the rejection in view of the heavy amendments & remarks and addressed remaining issues in the updated rejection below. Examiner respectfully maintains the rejection.
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Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1 & 17:
Step 1: the claims 1 & 17 are drawn to a system and a method respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial expception identification.
Claim 1
Mapping Under Step 2A Prong 1
1. A system, comprising: a processor in communication with a memory,
the memory including instructions, which, when executed, cause the processor to:
(1) receive, at the processor, an nth subset of operating data descriptive of a cyber-physical system, comprising an automated lane change control system for a vehicle, the vehicle being an autonomous vehicle, the nth subset of operating data being a subset of a total set of operating data descriptive of the cyber- physical system and collected during operation of the automated lane change control system, the nth subset of operating data being associated with a first utility score indicative of risks associated with retrieval and disclosure of the nth subset of operating data [A];(2) reconstruct, at the processor, an nth reconstructed model of the cyber-physical system using the nth subset of operating data by recovering an operational model of a cyber/physical process of the automated lane change control system and extracting one or more reconstructed parameters based on data contexts and corresponding operational conditions;
See Step 2A Prong 2 and Step 2B.
See Step 2A Prong 2 and Step 2B.
See Step 2A Prong 2 and Step 2B.
Abstract Idea/Mathematical Concept/Mental Step: For [A] The utility score is not gathered data. Mere association of this utility score to gathered data is not positive recitation. At best understood this is mathematical concept that computes the utility score somehow1.
Abstract Idea/Mathematical Concept/Mental Process: The reconstructed model is mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) as evident from specification [0028]2 and [0037]3 where the model is reconstructed using observable variables as disclosed in specification [0050]-[0051]. If the reconstruction is considered to involve evaluation, then this could be mental step performed with pencil and paper. E.g. evaluating equations in [0037] with data from [0050]-[0051]. (as per MPEP 2106.04(a)(2)(III)).
Use of processor is nominal where the computer is used as a tool to perform mental process. See MPEP 2106.04(a)(2)(III)(C)(1)-(3).
(3) evaluate, at the processor, a safety factor of the nth reconstructed model of the cyber-physical system, the safety factor being based on whether a reach set of the nth reconstructed model intersects an unsafe set;
(4) evaluate, at the processor, a second utility score associated with an accuracy factor of the nth reconstructed model and the safety factor of the nth reconstructed model; and
(5) identify, at the processor, an optimal subset of operating data of the total set of operating data descriptive of the cyber-physical system
from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4), the optimal subset minimizing risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of a safety analysis methodology used to generate the reach set.
Abstract Idea/Mathematical Concept/Mental Process: The evaluation step recites mental process where the evaluation to compute the safety factor can be performed based on the observed data (nth reconstructed model of the cyber-physical system) with pencil and paper (as in MPEP 2106.04(a)(2)(III)(A)). This may also be considered a mathematical calculation. (as in MPEP 2106.04(a)(2)(III)(A)).
Abstract Idea/Mathematical Concept/Mental Process: The evaluation step recites mental process where the evaluation to compute the second utility score can be performed based on the observed data (an accuracy factor of the nth reconstructed model and the safety factor of the nth reconstructed model) with pencil and paper (as in MPEP 2106.04(a)(2)(III)(A)). This may also be considered a mathematical calculation. (as in MPEP 2106.04(a)(2)(III)(A)).
Abstract Idea/Mathematical Concept/Mental Process: The identifying …an optimal subset of operating data step recites mental process of identifying based on observed data (total set of operating data) (as in MPEP 2106.04(a)(2)(III)(A)). This may also be considered a mathematical calculation. (as in MPEP 2106.04(a)(2)(III)(A)).
See Step 2A Prong 2 and Step 2B.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. That is, nothing in the claim element precludes the step from practically being performed in the mind or with the aid of pencil and paper but for the recitation of generic computer components (e.g. the claimed processor). Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper or computer as a tool.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1. A system, comprising: a processor in communication with a memory,
the memory including instructions, which, when executed, cause the processor to:
(1) receive, at the processor, an nth subset of operating data descriptive of a cyber-physical system, comprising an automated lane change control system for a vehicle, the vehicle being an autonomous vehicle [A] , the nth subset of operating data being a subset of a total set of operating data descriptive of the cyber- physical system and collected during operation of the automated lane change control system [B], the nth subset of operating data being associated with a first utility score indicative of risks associated with retrieval and disclosure of the nth subset of operating data [C];
(2) reconstruct, at the processor, an nth reconstructed model of the cyber-physical system using the nth subset of operating data by recovering an operational model of a cyber/physical process of the automated lane change control system and extracting one or more reconstructed parameters based on data contexts and corresponding operational conditions;
Under MPEP 2105.05(f) & (g) use of generic computer components (processor & memory) to perform data gathering and execution of generic model is does not integrate the claimed additional elements into practical application.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case the this is mere data gathering using conventional computer components.
Use of generic processor may be rejected under MPEP 2106.05(g). See Step 2A Prong 1 above.
As per [A] & [B] Specifying the data is from lane change just labels the data or provides source of data. This data could be simple as velocity of vehicle4. This data collection does not integrate the claim into practical application. Collection of data (e.g. velocity, position, steering angle) during operation also merely data gathering.
As per [C], See rejection under Step 2A Prong 1 above.
Under MPEP 2106.05(f)(1): The claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". The extracting one or more reconstructed parameters (parameter with values a-g) is idea of solution because while some specific numbers are shown5, specification fails to disclose how these parameters are reconstructed, based on data contexts and corresponding operational conditions. Blanket statement6 is not a solution. How are parameters a-g based on context and operational conditions?
(3) evaluate, at the processor, a safety factor of the nth reconstructed model of the cyber-physical system, the safety factor being based on whether a reach set of the nth reconstructed model intersects an unsafe set;
(4) evaluate, at the processor, a second utility score associated with an accuracy factor of the nth reconstructed model and the safety factor of the nth reconstructed model; and
(5) identify, at the processor, an optimal subset of operating data of the total set of operating data descriptive of the cyber-physical system
from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4),
the optimal subset minimizing risks associated with retrieval and disclosure [D]
while permitting reconstruction of the operational model [E]
and completion of a safety analysis methodology used to generate the reach set [F].
Use of generic processor may be rejected under MPEP 2106.05(g). See Step 2A Prong 1 above.
Under MPEP 2106.05(f)(1): The claim recites idea of solution. The disclosure lacks any detail of how the safety factor7 is computed. Further to determine what is unsafe, by performing reachability analysis on safety factor, when the specification of what safety factor includes, is a reach. In other words, there are no details of (1) how safety factor is computed (2) how reachability analysis is performed to determine what is unsafe.
Use of generic processor may be rejected under MPEP 2106.05(g). See Step 2A Prong 1 above.
Use of generic processor may be rejected under MPEP 2106.05(g). See Step 2A Prong 1 above.
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
In this case limitation [D] is an idea of solution because the specification does not disclose how to minimize the risks associated with retrieval and disclosure (or minimize utility score – see step (1)). As mentioned above the specification does not disclose how utility score is computed, let alone how it is minimized.
In this case limitation [E] is an idea of solution because the specification does not disclose any mechanism for permitting reconstruction of the operational model. The operational model as identified in [0062] & (Eq. 4) of disclosure simply exemplarily shows values of in (Eq.6), (Eq.7) and (Eq.10). and fails to show how the values for parameters a-g are determined.
Further
In this case limitation [F] is an idea of solution because specification fails to disclose (1) how the utility score is generated, (2) how reachability analysis is performed related to the utility score to determine the reach set, (3) how the intersection of the reach set to leads to the complete the safety analysis. These are all idea of solution under MPEP 2106.05(f).
Further even if implemented and one set of vehicle operating data is found to be safer than other, such data is based on per instance basis and does not improve the current driving (since the data is collected during the operation) and nothing is fed back or needs to be fed back since the instance of lane change has already occurred. This is therefore does not improve the technological art of lane change and at best identifies one dataset being superior than other based on preset safety analysis ( details of safety analysis being missing as identified above).
In particular, the claim(s) recites the additional elements of a processor for the system claim, at a high-level of generality (i.e. a generic processor performing generic functions of computing and executing information such that it amounts to no more than mere instructions to apply the exception using a generic computer component). Accordingly, 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. See MPEP 2106.05(f).
Additionally the claim as whole, even after accounting for additional elements as mapped in step 2A prong 2, do not integrate the abstract idea into a practical application because it does not recite improvement in functioning of a computer or any specific technology (as per MPEP 2106.05(a)). Further mere recitation that the data is related to a cyber-physical system is field of use (MPEP 2106.05(h)). The computing of first and second utility scores based on the collected subset or total operating data is akin to computation of alarm limit as in In re Flook.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer/processor to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer/processing component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)). Also step (1) is extra-solution activity (MPEP 2106.05(g)) related to data gathering. Further the additional elements do not add significantly more because they do not recite improvement in functioning of a computer or any specific technology (as per MPEP 2106.05(a)). Further mere recitation that the data is related to a cyber-physical system is field of use (MPEP 2106.05(h)). Hence, considering the same grounds as integration of the abstract idea into a practical application, the additional elements do not contribute significantly more for the same reasons. The claim 1 therefore is considered to be patent ineligible.
Claim 17 recites the method akin to system recited in claim 1. Claim 17 is rejected in similar manner.
Claims 2-7 recite various aspects related to determining which subset (nth or n+1th) is optimal subset based on the collective optimization of first and second index scores, which are related to accuracy, safety, risk scores (and further enumerated risk scores). These limitations merely add to algorithm to compute the various scores and are rejected as mental step under step 2A prong 1 (MPEP 2106.04(a)(2)(III)). Further evaluation under step2A prong 2 computation of these scores, associated to generically defined cyber physical system, is at best field of use under MPEP 2106.05(h). The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B). Amendment to claims only add limitations which are further considered an abstract idea adding to the determination whether the data sets are safe or not (while completely omitting how they are determined to be safe or how the indexes are computed, based on which safety is determined).
Claim 8-9 recites apply, at the processor, a safety analysis methodology to the nth reconstructed model resulting in the safety factor of the nth reconstructed model & wherein the safety analysis methodology includes a reach set analysis of the nth reconstructed model¸ the reach set analysis providing a set of states that the automated lane change control system can cover for a given set of initial conditions on continuous variables of the automated lane change control system, the set of states being the reach set.. This is considered as abstract idea under step 2A prong1 (mathematical concept/mental step). Under step 2A Prong 1, the reach set analysis based on set of states for lane change (presumably gathered data) is considered as abstract idea/mathematical concept (set theory to compute intersection between gathered data and some golden metric). No implementation of reach analysis is shown in disclosure. Further Under step 2A Prong 2, and step 2B, the reach set analysis is applied to any lane change technical field does not lead to improvement in the technical field of lane change (see MPEP 2106.05(a)) and is field of use at best (See MPEP 2106.05(h)). Therefore the claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B). Claim 19 is rejected with similar rationale as claim 8.
Claim 10 is directed to displaying the safety factor and accuracy factor in most generic manner and is rejected as extra (post) – solution activity under MPEP 2106.05(g). The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B). Evaluation of the safety and accuracy based on some inefficiency limit (?) is an abstract idea/mental step at best. One can compare the two datum and determine what is inefficient.
Claim 11-12 are directed to safe/unsafe declaration based on value of safety threshold and is considered as abstract idea (mathematical concept to compare/mental step to evaluate (safe/unsafe) based on observation (safety factor against threshold)) under MPEP 2106.04(a)(2)(I)(C) and MPEP 2106.04(a)(2)(III), based on some intersection of data set (set theory/mental step). The claims do not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B). Claim 13 related to accuracy, is rejected in a similar manner as claim 11-12, as it performs similar evaluation for the accuracy factor. The evaluation of unsafe set is mental step and/or mathematical concept of comparing reach set with some error limits.
Claim 14 generically recites mining method to extract modes and transitions of the nth set of data. This is at best considered as mental step of extracting data to get modes and transitions based on the observed nth set of data. Further, under step 2A Prong 2, and step 2B, the mining analysis is not applied to any specific technology/technical field such that it leads to improvement in the technical field (see MPEP 2106.05(a)) and generic citation of cyber physical system in view of parent claim is field of use at best (See MPEP 2106.05(h)). The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B). Claim 20 is rejected with similar rationale as claim 14.
Claim 15 recites identify, at the processor, one or more types of suggested operating data that can improve reconstruction of the nth reconstructed model based on identified observable variables and data contexts. This is considered a mental step (e.g. user suggests based on expert knowledge something that can improve reconstruction), under step 2A prong 1 & MPEP 2106.04(a)(2)(III). Claim 5 further recites display, at a display device in communication with the processor, information related to the one or more types of suggested operating data; and receive, from a manufacturer, a second subset of operating data including at least one of the one or more types of suggested operating data.. Displaying/receiving information related to the one or more types of suggested operating data (even from manufacturer) is rejected as extra (post) – solution activity of data gathering under MPEP 2106.05(g)/(f). The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim 16 performs the same analysis for nth subset and n+1th subset of data as in claim 1 and remains an abstract idea that is not applied to any practical application or adds significantly more to a technical field of use, as mapped in claim 1. The rejection here would be incorporated in similar manner as for claim 1 for n+1th data subset. The newly amended limitation “by selecting, from among evaluated subsets, a subset that minimizes risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of the safety analysis methodology“ is repeated from the claim 1. Selecting is mental process based on evaluation that something is minimized.
Claim 18 is rejected in similar manner as claim 6 as abstract idea to compute the risk score. The claim 18 may also be considered as an idea of solution (MPEP 2105.05(f)(1)) as claim does not disclose how the risk score is minimized, the risk factor being indicative of risks associated with retrieval and disclosure of the nth subset of operating data. Detailing what the risk is associated with does not cure the issue. The claim does not disclose any additional limitations that integrate the judicial exception into practical application (Step 2A Prong 2) or contribute significantly more (Step 2B).
Claim Rejections - 35 USC § 112(a) Written Description Requirement
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
MPEP 2161.01 states:
For instance, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1349-50, 94 USPQ2d at 1171 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention’s boundaries.") (citing Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1405-06); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed); Fiers v. Revel, 984 F.2d 1164, 1170, 25 USPQ2d 1601, 1606 (Fed. Cir. 1993) (rejecting the argument that "only similar language in the specification or original claims is necessary to satisfy the written description requirement").
Claim 1 recites:
1. A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:
(1) receive, at the processor, an nth subset of operating data descriptive of a cyber-physical system, comprising an automated lane change control system for a vehicle, the vehicle being an autonomous vehicle, the nth subset of operating data being a subset of a total set of operating data descriptive of the cyber- physical system and collected during operation of the automated lane change control system, the nth subset of operating data being associated with a first utility score indicative of risks associated with retrieval and disclosure of the nth subset of operating data [A];
(2) reconstruct, at the processor, an nth reconstructed model of the cyber-physical system using the nth subset of operating data [B];
(3) evaluate, at the processor, a safety factor of the nth reconstructed model of the cyber-physical system, the safety factor being based on whether a reach set of the nth reconstructed model intersects an unsafe set [C];
(4) evaluate, at the processor, a second utility score associated with an accuracy factor of the nth reconstructed model and the safety factor of the nth reconstructed model [A]; and
(5) identify, at the processor, an optimal subset of operating data of the total set of operating data descriptive of the cyber-physical system from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4), the optimal subset minimizing risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of a safety analysis methodology used to generate the reach set [D].
As per [A], The specification lacks written description how the utility score is computed based on the received data (assuming utility score is also not received). Simply stating that operating data being associated with a first utility score indicative of risks associated with retrieval and disclosure of the nth subset of operating data does not provide any basis how it is associated with operating data and how it is calculated. Specification & original claims have 39 instances of the term, none describing how it is computed.
Further Although the specification is replete with use of terms risk factor8, safety factor9, accuracy factor10, (A) none of the models are used to show how these factors are computed, (B) let alone how they are optimimzed (as in claim 1 step (5)) to come to identification of optimal subset of data. The risk factor related to monetary cost score, time cost score, human risk score, confidentiality risk score are only mentioned in claim 7. Specification fails to disclose how any of these factors or scores are computed, let alone based on the reconstructed model for any one of the embodiments. E.g. lane change embodiment does not mention any of these scores and how they are computed.
As per [B], Applicant has not mapped a single paragraph to address written description requirement. Mere arguments does not provide support. On the contrary - The specification lacks written description how the model is reconstructed. Taking the lane change embodiment as disclosed in specification [0037]. The model here is fashioned using set of equations recited in (1). However the specification [0037] [0049]-[0069] discussing automated lane change embodiment for the constructed model does not show how any of the core variables are reconstructed. See Eq.4-5, 6-7 and 10. Specifically here the reconstructed values of reconstructed parameters a-g associated with velocity (v), position (s), steering (w) and acceleration (a) from the initial model ([0037]) are not shown as reconstructed. In the response to arguments in Remarks (Pg.17 ¶2) applicant has stated that extraction of one or more reconstructed parameters [is done] using operating data. The specification does not describe any steps/methodology that shows how the reconstructed parameters are derived from operating data. E.g. See
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It is unclear how the sampling frequency is derived, how the Error bound is used in computation of variables d, e, and f (which make the reconstructed model). It is not even clear what is the reconstructed model?
An Enablement rejection is not made as it cannot be ascertained what is not enabled. Specifically the algorithm that computes the reconstructed model of the cyber physical system (CPS), and more specifically algorithm that computes values of a-f in Eq. 6 and Eq.7 cannot be ascertained form the disclosure. Values at this point is related to trial and error or expert knowledge. See specification [0064]:
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In reference to model disclosed in specification [0037]:
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As per [C], the amended limitation states safety factor being based on whether a reach set of the nth reconstructed model intersects an unsafe set. Its unclear what comprises the reach set, unsafe set, how they are calculated. Further it is unclear how the safety factor is computed based on intersection of a reach and unsafe sets? There are no examples what is in the reach and unsafe sets let alone methodology for computation of safety factor. This is new matter.
As per [D], the claim now claims that determination of optimal set from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4), the optimal subset minimizing risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of a safety analysis methodology used to generate the reach set. This is not supported by the specification. E.g. a quick review of specification shows that there is no methodology shown to compute the reach set (let alone what it comprises). Fig.7A-7G are graphs what do not show how the reach set is computed from one iteration to another.
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There is nothing in the specification that shows how the risk is minimized.
Claim 16 & 17 are rejected likewise.
Amended Claim 13 now recites:
13. (Currently Amended) The system of claim 10, wherein the safety information includes an indication that the nth subset of operating data is insufficient when an intersection between the reach set of the nth reconstructed model and the unsafe set is within error limits of an analysis process.
This is new matter. See specification ¶[0025][0065[0095]. They discuss operating data being insufficient, but not how insufficiency is determined (based on error limit).
Claim 14 recites
14. The system of claim 1, wherein the memory further includes instructions, which, when executed, cause the processor to: apply, at the processor, a cyber-physical system mining method to the nth subset of operating data resulting in the nth reconstructed model having an nth set of operating parameters including an nth set of response functions indicative of an nth set of modes and an nth set of mode transition conditions that dictate transitions between each mode of the nth set of modes.
The specification does not disclose use of the cyber physical mining method11 but fails to disclose what is this method. Generically illuding to Hymn12 lacks proper disclosure for any specific embodiment. Claim 20 reciting similar limitation is rejected with similar rationale.
Dependent claims 2-15 and 17-20 dependent on claim 1 and 16 respectively do not cure the deficiencies of claim 1 and 16 and therefore are rejected for inheriting those deficiencies.
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.
Claim 1 recites:
1. A system, comprising: a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:
(1) receive, at the processor, an nth subset of operating data descriptive of a cyber-physical system, comprising an automated lane change control system for a vehicle, the vehicle being an autonomous vehicle, the nth subset of operating data being a subset of a total set of operating data descriptive of the cyber- physical system and collected during operation of the automated lane change control system, the nth subset of operating data being associated with a first utility score indicative of risks associated with retrieval and disclosure of the nth subset of operating data;
(2) reconstruct, at the processor, an nth reconstructed model of the cyber-physical system using the nth subset of operating data[A].;
(3) evaluate, at the processor, a safety factor of the nth reconstructed model of the cyber-physical system, the safety factor being based on whether a reach set of the nth reconstructed model intersects an unsafe set;
(4) evaluate, at the processor, a second utility score associated with an accuracy factor of the nth reconstructed model and the safety factor of the nth reconstructed model; and
(5) identify, at the processor, an optimal subset of operating data of the total set of operating data descriptive of the cyber-physical system from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4), the optimal subset minimizing risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of a safety analysis methodology used to generate the reach set [B].
As per [A] reconstructing the nth reconstructed model would mean there is a model already existing. First, the claim does not disclose any model related to nth subset of operating data on basis of which the reconstructed model is made. Therefore it is unclear from where the model is reconstructed or what the model relates to (application?). Second, it is unclear what is the reconstructed model and how it is reconstructed. E.g. specification [0037] [0049]-[0069] discuss automated lane change embodiment. Here the reconstructed values of velocity (v), position (s), steering (w) and acceleration (a) from the initial model ([0037]) are not shown as reconstructed.
As per [B], First, the claim appears to be missing connection between the steps (3)-(4) which compute the safety and accuracy factors for nth subset and step (5) which does not involve any computation related to steps (3)-(4). Specifically step (5) appears to refer to total set of operating data to optimize the first utility score and the second utility score with no reference to nth subset. Secondly, the limitation of identifying … an optimal subset of operating data … from among the nth subset of operating data and an (n+1)th subset of operating data iteratively evaluated according to steps (1)-(4), the optimal subset minimizing risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of a safety analysis methodology used to generate the reach set does not determine how the optimization (minimization) is achieved and how the optimal subset is chosen. The no operation is performed on the operational data (data received) other than computing utility score. No mechanism is shown how the subsets of data are chosen. Thus the claim itself is indefinite how the minimization is performed. Claims 16-17 are rejected likewise.
Claim 2 recites “wherein the optimal subset of operating data is the nth subset of operating data of the total set of operating data when the nth subset of operating data minimizes risks associated with retrieval and disclosure while permitting reconstruction of the operational model and completion of the safety analysis methodology”. First this limitation is repeated from the claim 1 for most part. Second, It is unclear how any of the bolded aspects (minimizing, permitting reconstruction, completion of safety methodology) is achieved in the claim. These appear to goals the claim intends to achieve, not limiting how they are achieved..
Claim 5 recites “wherein the second utility score is optimized when the accuracy factor indicates that the nth reconstructed model is accurate enough and the safety factor indicates that the reach set of the nth reconstructed model does not intersect the unsafe set.” The claim recites a relative terminology (as bolded) and lacks metes and bounds what is to be considered as accurate enough. The claim is therefore indefinite. There is no methodology claimed (or in specification) to show how the second utility score is computed (ad hoc).
Claim 7 recites various risk scores, however it is unclear how these risk scores are calculated. The Specification lacks disclosure of any of these risk scores being disclosed and therefore computations and scope of these risk scores is indefinite. The amendment including “the risk factor being indicative of risks associated with retrieval and disclosure of the nth subset of operating data,…” does not cure this deficiency.
Claim 15 recites “identify, at the processor, one or more types of suggested operating data13 that can improve reconstruction of the nth reconstructed model; and display, at a display device in communication with the processor, information related to the one or more types of suggested operating data”. Amendment introduces “… based on identified observable variables and data contexts”. This however does not address the issue as it is still unclear who or what suggests the operating data based on data context. There is no methodology claimed. How is the suggested operating data identified? Specification as noted in footnote is silent on this and therefore the claim in view disclosure is indefinite.
Dependent claims 2-15 and 17-20 dependent on claim 1 and 16 respectively do not cure the deficiencies of claim 1 and 16 and therefore are rejected for inheriting those deficiencies.
Relevant Prior Art of Record
US 20190250617 A1 by Ford; Jordan et al. teaches receiving, by a controller, a command to navigate the vehicle, the command based on a global route calculation. The method further includes ranking, by the controller, maneuver patterns from a list of maneuver patterns to generate a ranked list of maneuver patterns (Abstract). Further this also teaches in [0035] In response, the trajectory planner 10 generates a ranked list 217 of maneuvers, at 210. In one or more examples, the list of maneuvers 217 may be a predetermined list of maneuvers that the trajectory planner 10 can select a maneuver from. For example, the list 217 may include different lane change options, such as changing the lane in which the host vehicle 50 is traveling to a lane-0, or lane-1 etc. The list 217 further includes keeping the current lane. The list 217 may further include an end-of-list (NULL) entry, which is a specific entry at the end of the list 217 to identify the end of the list 217. And in [0048] … The method includes computing a safety score for trajectories based on the closest approach to an obstacle, the required lateral and longitudinal acceleration, or other local properties of the trajectory. The objects considered when computing the safety score are local objects from a predetermined vicinity from the host vehicle 50, for example 100 meters, 50 meters, and the like.
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NPL by Chuchu Fan et al (“DryVR: Data-driven verification and compositional reasoning for automotive systems”, dated 22 Feb 2017) disclose a system which performs verifying hybrid control systems that are described by a combination of a black-box simulator for trajectories and a white-box transition graph specifying mode switches (Abstract). The reconstructed model as claimed would relate to the learning discrepancy (Pg.11 §3.1.3) associated with total set of operating data (mapped as 10-20 traces), where each trace has 10-10000 time points.
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The safety factor is computed with Safety Verification algorithm (Pg.2 §3.3) via GraphReach (Claim 9 mapped in §3.2). The accuracy score would be equivalent to correctness (Pg.12)
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The Fan prior art also uses the lane change operation (Fan: Pg.2) and generates the graph with transitions (as in claim 14 mapped to Fan Fig.1)
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The detailed mapping of the claim 1 and 16 is not made as prior art rejected because details of the model reconstruction (issues with 35 USC 112 1st and 2nd) cannot be clearly resolved.
US PGPUB No. 20250013921 by Moradi; Farnaz et al. teaches source machine learning (ML) model selection for the transfer learning. A method may include receiving a source ML model request from a target domain, determining candidate source ML models, calculating a model quality score for each of the candidate source ML models, using the calculated model quality scores to select candidate source ML models, sending the selected candidate source ML models to the target domain, receiving fine-tuned ML model weights for fine-tuned ML models, and calculating a model quality score for each of the fine-tuned ML models.
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US 11991050 B2 by Hicks; Andrew C. M. et al. teaches Model 402 (e.g., a first model) and model 404 (e.g., a second model) can be deployed in a traffic camera in the first US state to detect license plates of the first US state. For example, model 402 and model 404 can ingest new data 103 wherein new data 103 can comprise license plates of the first US state, the second US state and other US states. Model 402 can generate output 503 wherein output 503 can be analyzed by verification component 108 to verify that model 402 can exhibit about a 97% accuracy (i.e., first accuracy 506) in detecting license plates of the first US state in real-time. Similarly, model 404 can generate output 505 wherein output 505 can be analyzed by verification component 108 to verify that model 404 can exhibit about an 85% accuracy (i.e., second accuracy 508) in detecting license plates of the first US state in real-time. Computation component 110 (FIG. 1) can generate a second ratio (e.g., second ratio 117) wherein the second ratio can be a ratio of first accuracy 506 and second accuracy 508 (about 1.141), to enable detection of data drift in an edge device (e.g., edge device 120 of FIG. 1) deployed without network connectivity.
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Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Tuesday, July 14, 2026
1 Specification and original claims have 39 instance of “utility score”, none describe how the utility score is computed based on the retrieved data. For purposes of rejection under 35 USC 101, it is assumed since this is not received and computed based on received data.
2 Specification [0028] "... The reconstructed model 240 can include sets of response functions, sets of modes and sets of mode transition conditions descriptive of the CPS...."
3 Specification [0035] showing sample model for the lane change system.
4 Specification [0037], [0078] "...The velocity was already measured by the speedometer of the car....".
5 Specification [0064], [0085]
6 Specification [0062] "... Here, d, e, f, g are known, while b and c are the only unknowns. Hence with at least two test cases with different v.sub.x(0) and s.sub.x(0), the reconstructed parameters b and c can be obtained...."
7 Specification [0028], [0040], [0070], [0092]-[0094] show no details of how the safety factor is computed.
8 Risk factor in specification ¶[0090] and claims
9 Safety factor in specification ¶[0028], [0040], [0070], [0092]-[0094] and claims
10 Accuracy factor in specification ¶[0028]-[0033], [0040], [0054], [0065] and [0094] and in claims
11 Cyberphysical mining method in specification ¶[0028] [0030] [0049] [0091]
12 Hymn (see IDS filed 3/29/23 reference 7) is not directed to lane change application as in the current disclosure.
13 “suggested operating data” in specification ¶[0049][0090]
Prosecution Insights