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 .
DETAILED ACTION
Claims 1-7 have been examined and claims 8-20 are withdrawn from consideration.
Election/Restrictions
Applicant’s election without traverse of claim 1-7 (Invention 1) in the reply filed on February 17, 2026 is acknowledged.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of classifying and managing data regarding telematics unit behavior and vehicle environment parameters without significantly more.
Claim 1: The claim(s) recite(s) a classification machine learning model adapted to receive TCU behavior parameters and environment parameters and output a usage scenario and a QoS level according to the TCU behavior parameters and the environment parameters. This judicial exception is not integrated into a practical application because the claim only recites a high-level ML model performing generic data processing, classification, and decision-making without a particular inventive application to a technical solution outside of abstract data manipulation. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited TCU and vehicle hardware elements are generic and merely perform the abstract idea without any novel hardware configuration or unconventional application that improves computer or network functionality.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of updating controlling parameters based on classified scenarios and QoS levels without significantly more. The claim(s) recite(s) a reinforcement learning agent adapted to update controlling parameter policy according to the usage scenario and the QoS level. This judicial exception is not integrated into a practical application because the reinforcement learning agent is recited at a high level, performing generic optimization without a particular inventive implementation tied to a technical improvement of the TCU or network hardware. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the agent interacts with generic controlling parameters without improving the underlying telematics system or providing a technological effect beyond abstract data-driven decision-making.
Claim 3 does not recite additional subject matter beyond that of claim 2 that would confer patent eligibility. Dependent claim 3 recites rules for adjusting controlling parameters according to the usage scenario and the QoS level, which is a mental process or abstract data manipulation. Accordingly, claim 3 is not patent eligible under 35 U.S.C. 101.
Claim 4 does not recite additional subject matter beyond claim 1. The environment parameters such as location, weather, time of day, and signal coverage map are data inputs processed by the ML model. Claim 4 is directed to an abstract idea and does not include significantly more.
Claim 5 does not recite additional subject matter beyond claim 1. The usage scenario describing vehicle location, trajectory, surroundings, and network traffic is abstract information classification and manipulation. Claim 5 is not patent eligible under 35 U.S.C. 101.
Claim 6 does not recite additional subject matter beyond claim 1. The TCU behavior parameters of signal quality and latency are data measurements processed by the ML model, which is an abstract idea. Claim 6 does not include significantly more.
Claim 7 does not recite additional subject matter beyond claim 1. Receiving parameters from vehicle sensors and other vehicles is merely gathering data for processing by the ML model, which is an abstract idea. Claim 7 does not include significantly more.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 1-7 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim Claim 1, limitation “classification machine learning model adapted to receive TCU behavior parameters and environment parameters and output a usage scenario and a QoS level”: The specification describes example inputs/outputs but does not fully disclose the architecture, training data, or algorithmic details required to implement the ML model across the full claimed scope.
As per Claim 2, limitation “reinforcement learning agent adapted to update controlling parameter policy”: The specification provides no specific learning policy table or reward function parameters, which may be required to enable full scope of updating controlling parameters under all scenarios.
As per Claims 3–7, while illustrative examples are provided, the specification does not describe how to configure the controlling parameter rules, sensor fusion, or integration of multi-vehicle data to cover all claimed scenarios, raising enablement concerns.
As per claims 2-7, they are also rejected based on the dependency of claim 1.
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 1-7 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.
As per Claim 1: “classification machine learning model,” “usage scenario,” and “QoS level” are functional language without corresponding structure or algorithm fully disclosed, making the claim indefinite.
As per Claim 2: “reinforcement learning agent adapted to update controlling parameter policy” is functional language lacking algorithmic detail, such as specific learning rules, convergence criteria, or reward structure, rendering it indefinite.
As per Claim 3: “set of rules for adjusting controlling parameters” is indefinite because no particular rules or rule format is disclosed.
As per Claims 4–7: terms like “environment parameters,” “TCU behavior parameters,” and “sensors of other vehicles” are broad and not clearly defined, creating uncertainty as to what is covered.
As per claims 2-7, they are also rejected based on the dependency of claim 1.
Claim limitation:
Claim 1, limitation “classification machine learning model adapted to receive… and output… usage scenario and QoS level”:
Claim 2, limitation “reinforcement learning agent adapted to update controlling parameter policy”:
Claim 3, limitation “set of rules for adjusting controlling parameters”:
Claims 4–7: Functional terms “comprise environment parameters…”, “describe location and trajectory…”, “received from sensors of the vehicle and other vehicles”
, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
Claim 1, limitation “classification machine learning model adapted to receive… and output… usage scenario and QoS level”: the claim function as: Classification of TCU behavior and environment parameters to determine usage scenario and QoS level (ML model 502, usage scenario classifier 512, QoS classifier 514, inputs 504, outputs 506). Specification describes an ML model generically; does not disclose algorithm, architecture, or training sufficient for full scope of the claim.
Claim 2, limitation “reinforcement learning agent adapted to update controlling parameter policy”: the claim function as: Update controlling parameter policy according to QoS and usage scenario (reinforcement learning agent 602, rewards, convergence concept). No specific algorithm, reward function, or convergence mechanism disclosed; claim may cover all reinforcement learning agents.
Claim 3, limitation “set of rules for adjusting controlling parameters”: the claim function as: Adjust controlling parameters according to usage scenario and QoS level (controlling parameter policy 524). Specification does not disclose how rules are constructed or applied in all scenarios; functional language without sufficient corresponding structure.
Claims 4–7: Functional terms “comprise environment parameters…”, “describe location and trajectory…”, “received from sensors of the vehicle and other vehicles” lack specific structure or algorithm to perform the function, relying solely on generic sensors and data acquisition.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dribinski et al. (US 2020/0067793)
As per claim 1, Dribinski teaches:
A quality of service (QoS) management system of a telematics control unit (TCU) of a vehicle” (Paragraph 3: vehicle telematics system providing communication-related services with QoS requirements; Paragraph 45: selecting a cell fitting QoS requirements
“a model” (Paragraph 64: predictive model generated using collected data; Paragraph 81: machine learning techniques used to generate weighting matrix)
“adapted to receive TCU behavior parameters and environment parameters” (Paragraphs 49–50: receiving vehicle position, road conditions, sensor data; Paragraph 57: location, speed, RAT-related measurements; “environment parameters” met by location, speed; “TCU behavior parameters” met by RAT-related measurements / KPIs)
“and output a usage scenario and a QoS level according to the TCU behavior parameters and the environment parameters” (Paragraph 64: predictive model enabling selection of desirable cells based on location, speed, services, and measurements; Paragraph 81: QoS-related classification of cells; “usage scenario” met by combination of location, speed, and service conditions; “QoS level” met by QoS classification of cells).
Dribinski does not explicitly disclose:
a “classification machine learning model” as explicitly labeled; and/or
explicitly outputting both “a usage scenario and a QoS level” as separate outputs
It would have been obvious at the time the invention before the effective filing date of the claim invention was made configure the predictive model of Dribinski as a classification machine learning model that explicitly outputs: a usage scenario, and a QoS level because:
Predictive models inherently perform classification and/or regression tasks:
Dribinski already discloses QoS-related classification (Paragraph 81) and Converting or structuring outputs into labeled classifications (e.g., “usage scenario,” “QoS level”) is a routine design choice
Explicit separation of outputs is a matter of data representation
Dribinski’s model considers inputs such as location, speed, and services (Paragraph 64) and representing these as a “usage scenario” is merely an abstraction or labeling of known inputs
Motivation to improve interpretability and control
Structuring outputs into discrete classifications would have predictably improved system transparency, policy decision-making and QoS optimization control
Well-understood, routine, and conventional ML practices
Classification models producing labeled outputs were widely known and no criticality is associated with the specific naming of outputs
As per Claim 2, Dribinski teaches: reinforcement learning agent adapted to update controlling parameter policy
DRIBINSKI discloses:
continuous updating of predictive model (Paragraph 65)
applying updated model to adjust connectivity (Paragraph 66)
DRIBINSKI does not explicitly disclose a reinforcement learning agent, it would have been obvious to implement the model update using reinforcement learning because:
reinforcement learning is a known technique for adaptive policy optimization
DRIBINSKI already performs iterative improvement of connectivity decisions
substitution of one known ML technique for another yields predictable results
In other words, the invention of DRIBINSKI would have been obviously show:
a reinforcement learning agent adapted to update controlling parameter policy according to the usage scenario and the QoS level.
As per Claim 3, Dribinski teaches: controlling parameter policy is a set of rules
DRIBINSKI discloses:
predictive model governing connectivity decisions (Paragraph 65–66)
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to implement such decisions as rule-based policies, as this is a conventional implementation of decision systems.
In other words, the invention of DRIBINSKI would have been obviously show:
the controlling parameter policy is a set of rules for adjusting controlling parameters according to the usage scenario and the QoS level.
As per Claim 4, Dribinski teaches: at least one of environment parameters comprise location, weather, time of day, or signal coverage map
DRIBINSKI discloses:
location and measurements (Paragraph 57)
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to include additional environmental parameters such as weather and time of day because:
such parameters are known to affect wireless signal quality
their inclusion would have predictably improved QoS decisions
In other words, the invention of DRIBINSKI would have been obviously show:
the environment parameters comprise at least some of location, weather, time of day, and signal coverage map.
As per Claim 5, Dribinski teaches: usage scenario describes location, trajectory, surroundings, and network traffic
DRIBINSKI discloses:
location, speed, service conditions (Paragraph 64)
It would have been obvious at the time the invention before the effective filing date of the claim invention was made to formalize these into a “usage scenario,” as this is merely organizing known inputs into a conceptual grouping.
In other words, the invention of DRIBINSKI would have been obviously show:
the usage scenario describes location and trajectory of the vehicle, surroundings, and network traffic.
As per Claim 6, Dribinski teaches: TCU behavior parameters comprise signal quality and latency
DRIBINSKI discloses:
RAT-related measurements / KPIs (Paragraph 57)
These inherently include signal quality and latency metrics.
In other words, the invention of DRIBINSKI would have been obviously show:
the TCU behavior parameters comprise signal quality and latency.
As per Claim 7, Dribinski teaches: parameters received from sensors of the vehicle and other vehicles
DRIBINSKI discloses:
data from sensors, ECUs, and external systems (Paragraph 49–50)
Use of additional vehicle-to-vehicle sources would have been obvious as part of known V2X systems.
In other words, the invention of DRIBINSKI would have been obviously show:
the TCU behavior parameters and the environment parameters are received from sensors of the vehicle and other vehicles.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOI C LAU whose telephone number is (571)272-8547. The examiner can normally be reached on Monday-Friday, 8:30am-5:00Pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached on (571)272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HOI C LAU/Primary Examiner, Art Unit 2689