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
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 7/9/2026 with respect to the rejections under 35 USC 101 have been fully considered but they are not persuasive.
Claims 1-2, 4-8, 10-14, 16-17, and 19-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed to non-statutory subject matter. In particular, the Office submitted that claims 1-2, 4-8, 10-14, 16-17, and 19-20 are "directed to an abstract idea without significantly more." Office Action, p. 9.
Under Step 2A, Prong I, the Office asserts that the previously-presented claim limitations "process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, wherein the first machine-learning model is trained to determine relevant information based on V2X information and queries; and process the query and the determined subset of the plurality of the V2X messages" constitute a mental process. See Office Action, pp. 10-11. Additionally, the Office compares previously-presented claim 1 to Claim 2 of Example 47 of the 101 Subject Matter Eligibility Guidance (SMEG). See Office Action, pp, 2-4 and 1 Under Step 2A, Prong II, the Office asserts that the additional claim recitations do not integrate the inventive concept into a practical application. See Office Action, pp. 11-13. Under Step 2B, the Office asserts that previously-presented claim 1 does not include additional elements that are sufficient to amount to significantly more than an abstract idea. See Office Action, pp. 11- 13.
Examiner has included the above to provide context for the following arguments.
Example 42 of the SMEG
Even assuming, for the sake of argument, that some portion of the claims recites an abstract idea, the claims as a whole integrate any such abstract idea into a practical application. Specifically, the claims recite an improvement to the functioning of a vehicle computing system - not merely the application of an abstract idea to a vehicle context. The USPTO recognizes that a claim is integrated into a practical application where it reflects an improvement to the functioning of a computer or an improvement to another technology. See MPEP § 2106.05(a). For example, SMEG claim 1 of Example 42 is patent eligible because the claimed structure improves the way a computer stores and retrieves data, thereby improving the functioning of the computer itself, rather than merely using a computer to implement an abstract idea.
The instant claims are directly analogous to claim 1 of Example 42. Vehicle-to-everything (V2X) communication environments present a concrete technical problem: surrounding vehicles and objects continuously broadcast V2X messages at high frequency and volume, each comprising multiple structured data fields. A vehicle computing system that naively passes the full, unfiltered stream of V2X messages to a large language model (LLM) to respond to an occupant query would suffer from severe computational inefficiency - the LLM would be forced to process large quantities of irrelevant data, degrading response latency, accuracy, and system resource utilization. See Instant Application, para. [0047].
Claim 1 recites a specific technical solution to this concrete technical problem: a two-stage machine-learning pipeline in which a first machine-learning model - specifically trained on V2X information and queries - processes the incoming V2X message stream and extracts a relevant field-level subset before that subset is passed to the second machine-learning model (e.g., an LLM) to generate a response. This architecture is not a generic arrangement of two models performing sequential abstract steps. Rather, the first model functions as a technically specialized pre- processor that is tailored to the structure and semantics of V2X messages, reducing the computational burden on the downstream LLM and constraining its inputs to contextually-relevant vehicular data. The result is a vehicle computing system that operates more efficiently and accurately than prior approaches that did not employ such a trained, domain-specific filtering stage See Instant Application, paras [0131]-[0133] (describing the specific training methodology for the first machine-learning model using V2X information and occupant queries).
This is similar to the improvement recognized as patent-eligible in Example 42 of the SMEG. Just as the self-referential table in Example 42 improved the way a computer stores and retrieves data - not by using a computer as a tool to implement an abstract idea, but by reconfiguring the computer's own data-handling architecture - the instant claims improve the way a vehicle computing system processes real-time V2X data by introducing a trained, domain- specific filtering stage that reduces the input space of the downstream LLM. The improvement is to the technology itself, not merely to the result the technology produces.
Accordingly, the claims as a whole integrate any abstract idea into a practical application through an improvement to the technical functioning of the vehicle computing system, and are therefore patent-eligible under Step 2A, Prong II.
Examiner respectfully disagrees. The asserted analogy between Example 42 Claim 1 and the present claimed invention is not found to be persuasive, as the present claimed invention appears to more closely relate to the implementation of a system for determining results by a computer, rather than altering or otherwise improving the functioning of the computer itself. In Example 42 Claim 1, the Claim was found to be patent-eligible as it was determined to “allow[ing] remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user” which went beyond the mere updating of medical records by reciting a specific configuration of sharing information by the computer system. Examiner notes that Claim 2 of Example 42 is not patent-eligible under the rationale that while information sharing is recited, the claims merely comprise generic instructions to apply a concept of sharing information, rather than a specific improvement over prior art systems.
As will be discussed in further detail below with respect to Example 3 of the subject matter eligibility guidelines, the present claimed invention appears, under its broadest reasonable interpretation, to encompass a technique of determining relevant data and processing that data to determine an answer using a computer, but in such a manner that would be capable of being performed by a human, such that the claimed invention appears to be computer implementation of an abstract idea, rather than the improvement of the computer technology itself. This differs from Example Claim 42 which specifically relates to the functioning of how a computer system stores and shares data – that is to say, the functioning of the computer system itself to share data is the end goal of the claimed invention in the Example Claim, rather than the processing means as in the present claimed invention. While the present claimed invention uses V2X messages to share information, the use of said messages appears to be relegated to data gathering, said data being subsequently processed, rather than the point of improvement for the system. Further, the asserted improvements in the efficiency of processing data in the present claimed invention appear to be improvements in the sequence of data processing by a computer, rather than inherent improvements to the functioning of the computer system itself as is the case in Example 42, in which the sharing of data was improved by the standardized format. Thus, Applicant Arguments set forth above with respect to Example 42 Claim 1 are not persuasive.
Should the Office maintain that the claims do not integrate the abstract idea into a practical application, the claims nonetheless recite significantly more than the abstract idea under Step 2B. The two-stage ML pipeline - comprising a first model specifically trained on V2X information and a second model operating on the filtered output of the first - represents an unconventional technical arrangement not routine or conventional in the field at the time of the invention. The examiner has not identified any prior art disclosing this specific two-model architecture applied to V2X message processing. Indeed, the examiner's prior art analysis confirms that the closest references each fail to disclose a system employing first and second machine-learning models operating in sequence on V2X messages, with the output of the first passed to the second. This non-routine, non-conventional technical combination constitutes significantly more than any abstract idea.
Examiner respectfully disagrees. While no prior art has been identified to specifically anticipate the present claimed invention, Examiner respectfully submits that this alone does not render the claim patent-eligible. While the prior art does not identify application of the claimed architecture as applied to a vehicle system using V2X messages and machine learning models, nor appear to render such limitations anticipated or obvious, this determination is decoupled from the determination of if the claim is eligible under 35 USC 101 for reciting an abstract idea without significantly more. As noted elsewhere in responses to arguments, the sequence recited appears to merely encompass evaluating and outputting data in a specific manner, and therefore the claim as a whole is asserted by the Examiner to be ineligible under 35 USC 101.
Example 3 of the SMEG
Applicant submits that the Instant claims are similar to the patent eligible claims of Example 3 of the SMEG. For example, the SMEG, in describing the patent-eligible Claim 1 of Example 3, states "[t]he claim also recites the additional steps of comparing the blue noise mask to a gray scale image to transform the gray scale image to a binary image array and converting the binary image array into a halftoned image. These additional steps tie the mathematical operation (the blue noise mask) to the processor's ability to process digital images. These steps add meaningful limitations to the abstract idea of generating the blue noise mask and therefore add significantly more to the abstract idea than mere computer implementation... the claim goes beyond the mere concept of simply retrieving and combining data using a computer" See SMEG - 101 Examples 1 to 36, p. 9 (emphasis added).
Similarly, the instant claims add meaningful limitations to processing V2X messages using machine-learning models. For example, the instant claims recite "process[ing] the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, ... process[ing] the query and the determined subset of the plurality of the V2X messages using a second machine-learning model to generate a response to the query; and use the response to perform at least one of: outputting the response to the occupant of the vehicle using a display of the vehicle, outputting the response using a vocalization system of the vehicle, or adjusting an operating parameter of the vehicle based on the response" These limitations tie the processing of data using machine-learning models to the ability of a computer to understand a scene and generate responses to queries about the scene.
Examiner respectfully disagrees. The processing of V2X messages as recited by the present claimed invention appears, under the broadest reasonable interpretation of the claim, to encompass the receipt and processing data for use in the determination of a response to the occupant query, or in other words the pre-solution activity of data gathering which does not render a claim patent-eligible as set forth in MPEP 2106.05(g). While the V2X messages recited by the present claimed invention may be received by a vehicle computing system, and processed by such, Examiner respectfully disagrees that the computer’s ability to understand and generate responses to a scene are inherently linked to the vehicle’s capability to process V2X messages in a manner similar to Example 3 of the subject matter eligibility guidelines. Under the broadest reasonable interpretation of V2X messages, the messages merely comprise generalized data in a plurality of fields readily understandable by the vehicle processing system, without indication that the information contained within would be inherently only usable by a computer, or otherwise so inherently related to the functioning of a computer that a person could not interpret similar data to understand a scene. Thus, Applicant arguments are not persuasive.
Additionally, in describing the patent-eligible claim 1 of Example 3, states "viewing the claim elements as an ordered combination, the steps recited in addition to the blue noise mask improve the functioning of the claimed computer itself. In particular, as discussed above, the claimed process with the improved blue noise mask allows the computer to use to less memory than required for prior masks, results in faster computation time without sacrificing the quality of the resulting image as occurred in prior processes, and produces an improved digital image." See SMEG - 101 Examples 1 to 36, p. 9 (emphasis added).
Similarly, the instant claims improve the functioning of a computer by "process[ing[ the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, ... [and] process[ing] the query and the determined subset of the plurality of the V2X messages using a second machine-learning model to generate a response to the query."
As explained in the as-filed specification "providing an LLM with too much context information and/or irrelevant context information may affect the ability of the LLM to generate an appropriate response." See Instant Application, para. [0047]. In contrast, the claims describe a system that "may filter the V2X messages to determine relevant V2X information. The systems and techniques may provide the relevant V2X information to an LLM and obtain a response to the query based on the relevant V2X information." See Instant Application, para. [0049]. Thus the claims describe a system that improves the ability of a computer to understand a scene and provide responses to queries about the scene.
Accordingly, because the instant claims include limitations that tie the processing of data to an ability of a computer (similar to Example 3 of the SMEG) and improve the ability of a computer (similar to Example 3 of the SMEG), Applicant submits that the instant claims are patent eligible under 35 U.S.C. § 101.
Examiner respectfully disagrees. Applicant asserts above that the instant claims improve the functioning of the computer by processing the messages to determine a relevant subset prior to determining a response to the query, improving the functioning of the computer by avoiding overwhelming the LLM with too much context information. Examiner respectfully asserts that these limitations/asserted improvements are not so inherent to the functioning of a computer as to alone constitute an improvement in computer technology. Limiting information provided for analysis to a second model by filtering out all but the potentially relevant data via a first model may be analogized to a human first searching for relevant information prior to evaluating said information to determine a response to a query, increasing the efficiency of said activity, which is to say, the problem solved by way of the present claimed invention is not computer-specific, but merely recites a generally applicable framework by which a determination of an answer to a query may be made more efficient.
Notably, with respect to Example 3 of the subject matter eligibility examples, the claims at issue were found to be eligible because “the instant claim is not merely limiting the abstract idea to a computer environment by simply performing the idea via a computer (i.e., not merely performing routine data receipt and storage or mathematical operations on a computer), but rather is an innovation in computer technology, namely digital image processing, which in this case reflects both an improvement in the functioning of the computer and an improvement in another technology” in the context that “the claimed process with the improved blue noise mask allows the computer to use to less memory than required for prior masks, results in faster computation time without sacrificing the quality of the resulting image as occurred in prior processes, and produces an improved digital image” as referenced by the Applicant in arguments above. In the present case, however, in the view of the Examiner, the claims appear to fall closer to “performing routine data receipt and storage or mathematical operations on a computer” than the improvements in the functioning of a computer found in Example 3 of the Examples.
In the Examples, the blue noise mask and the processing of digital images are fundamentally linked to the processor’s ability to perform said digital image processing (“These additional steps tie the mathematical operation (the blue noise mask) to the processor’s ability to process digital images,” Page 9 Paragraph 2)] which links the processing of images as recited by the example to a computer system from the start. An improvement to the processing, such as a new technique to use less memory, therefore inherently improves the functioning of the computer, and is therefore patent-eligible. In the present claimed invention, however, the claims are not so bound or limited, the invention being directed to the field of processing and responding to queries, which is a process routinely performed in human interaction. Crucially, a human could [and routinely does] perform steps correlating to those claimed as the improvement in the present claimed invention, by first narrowing the scope of data searched in response to a query prior to searching said narrowed information for the answer to the query. In this way, the claimed improvement to the functioning of the computer asserted in arguments is not an improvement inherently related to the computer, but an efficient way of processing information implemented by a computer, which does not render the claim patent-eligible as in Alice. Thus, Applicant arguments are not persuasive.
Example 47 of the SMEG
The instant claims differ from Claim 2 of Example 47 of the SMEG in significant ways. First, the SMEG, in describing Claim 2 of Example 47, states that the additional elements of claim 2 "do not integrate the recited judicial exception into a practical application." See SMEG - July 2024 Subject Matter Eligibility Examples, p. 9.
In contrast, instant claim 1 recites, in part, "process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, wherein the first machine- learning model is trained to determine relevant information based on V2X information and queries; process the query and the determined subset of the plurality of the V2X messages using a second machine-learning model to generate a response to the query; and use the response to perform at least one of: outputting the response to the occupant of the vehicle using a display of the vehicle, outputting the response using a vocalization system of the vehicle, or adjusting an operating parameter of the vehicle based on the response." Such recitations integrate the inventive concept of claim 1 into the practical application of question- answering, specifically question-answering based on scene understanding based on a specific category of data - V2X messages.
Examiner respectfully disagrees. The claims, given their broadest reasonable interpretation, recite the evaluation of data to determine a result, said result being subsequently output using a display or vocalization of the vehicle [in the relevant prongs of the present claimed invention for which 35 USC 101 applies]. As noted by MPEP 2106.05(g), the output of data determined as a result of a mental process [in the present instance, the output of a determined response to the query] is insignificant extra-solution activity, that causes the claim to merely recite gathering, processing, and outputting data, which cannot render a claim patent-eligible. While the response may be output via auditory or visual means by corresponding electronic devices, the Examiner interprets these limitations as mere instructions to apply the exception using generic devices well-known in the art, which also cannot render a claim patent-eligible. Thus, the above arguments are not persuasive.
Second, the SMEG, in describing Claim 2 of Example 47, states "[t]he trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions." See SMEG - July 2024 Subject Matter Eligibility Examples, p. 9 (emphasis added).
In contrast, instant claim 1 recites, in part, "process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, wherein the first machine- learning model is trained to determine relevant information based on V2X information and queries." Thereby placing meaningful limits on the recited operation and training of the machine- learning model. For example, the claimed machine-learning model operates on V2X messages, to determine relevant V2X messages from among V2X messages.
Accordingly, because the instant claims integrate an inventive concept into a practical application (unlike Example 47 of the SMEG) and place meaningful limits on the recited operation and training of the machine-learning model (unlike Example 47 of the SMEG), Applicant submits that the instant claims are patent eligible under 35 U.S.C. § 101.
Examiner respectfully disagrees. The use of machine learning models to process specifically V2X messages appears, under the broadest reasonable interpretation of the claim, to apply the claimed invention to a specific environment of field, which as set forth in MPEP 2106.05(g) does not render a claim patent-eligible [MPEP 2106.05(g), Selecting a particular data source or type of data to be manipulated: Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., (Fed. Cir. 2016)]. Thus, the application of the claimed invention using V2X messages does not render the claimed invention one which is practically applied, but rather constrains the claimed invention to a particular environment. Thus, Applicant arguments are not persuasive.
Accordingly, Applicant requests withdrawal of the rejection under 35 U.S.C. § 101.
For at least the reasons set forth above, as well as those below with respect to the specific rejection(s), Examiner respectfully maintains the rejections of the claims under 35 USC 101.
Allowable Subject Matter
Claim 15 is allowed.
The following is an examiner’s statement of reasons for allowance: With respect to Independent Claim 15, the claim recites at least the following limitations:
at least one memory; and at least one processor coupled to the at least one memory and configured to:
obtain a plurality of vehicle to everything (V2X) messages, each V2X message of the plurality of V2X messages comprising a plurality of fields;
obtain a query from an occupant of a vehicle;
process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, wherein the first machine-learning model is trained to determine relevant information based on V2X information and queries;
process the query and the determined subset of the plurality of the V2X messages using a second machine-learning model to generate a response to the query; and
adjust an operating parameter of the vehicle based on the response to the query, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, an automatic braking parameter for operating one or more brakes of the vehicle, or a lane change parameter for causing the vehicle to navigate from a first lane to a second lane.
No single prior art reference, nor combination of references, has been found to anticipate the limitations, when read in view of the remaining limitations of the claim. Particularly, no single prior art reference, nor combination of references, has been found to anticipate wherein a vehicle computing device is configured to determine a subset of V2X messages that contain relevant information to an occupant query, the subset comprising at least one field of at least one V2X message of the plurality of V2X messages, with the determined V2X message subset being subsequently passed to a second machine-learning model to generate a response to the query, as recited by the present claimed invention, much less wherein an operating parameter of the vehicle is adjusted based on the response to the query, the operating parameter being associated with at least one of a path for the vehicle to travel, an automatic braking parameter for operating one or more brakes of the vehicle, or a lane change parameter for causing the vehicle to navigate from a first lane to a second lane as recited by the present claimed invention.
The closest prior art found is Beaurepaire (US 2022/0122456 A1), Lee (US 2021/0157871 A1), and Li (CN 117807199 A), Beaurepaire reciting an apparatus for responding to a user query through the analysis of V2V or V2X messages received, Lee reciting a machine learning model used for processing and responding to a user query, and Li reciting a dialogue response method, including the determination of information relevant to the query.
More particularly, Beaurepaire recites an apparatus for responding to a user query regarding the state of the environment around the vehicle, including the exchange of information via V2V/V2X communication, and the receipt of a question from a vehicle user. Information may be identified in relation to the query, and possible answers to the user question may be determined on the basis of the acquired data. The response to the user query may include information relating to other vehicles, such as rationale for said other vehicle’s parking permissions. Beaurepaire however appears to be silent regarding wherein the identification of relevant information and the processing of the query to determine a response take place through the use of a machine learning model, much less wherein first and second machine learning models are used to determine relevant information and determine a response to the query in different steps from one another, the output of the first machine learning model being passed to the second machine learning model, as recited by the present claimed invention.
Lee recites a learning processor which collects information regarding a user query and environmental information, which may include V2X information. A machine-learning model may be used to process the query to determine related information to be provided to a user device. Communication hardware may be utilized to acquire the information, including the V2X information used in the evaluation. Lee however appears to be silent regarding wherein the system comprises first and second machine learning models, used to determine relevant information and determine a response to the query in different steps from one another, the output of the first machine learning model being passed to the second machine learning model, as recited by the present claimed invention.
Li recites a dialogue method, including the extraction of documents and the like with the highest relevance the question posed to the system. Upon the extraction of the text with the highest relevance to the posed question, the relevant text portions are passed to the prompt engineering template in order to obtain an answer result. Li however appears to be silent regarding wherein the dialogue system is implemented in a vehicle, much less wherein the relevance determination includes the filtering of V2X messages received by a first machine learning model, which then passes the relevant messages to a second machine learning model to determine a response to the query, as recited by the present claimed invention.
With respect to subject matter eligibility under 35 USC 101, while Independent claim(s) 1 & 16, are rejected under 35 USC 101 for being directed towards an abstract idea/mental process, the limitation of “adjust an operating parameter of the vehicle based on the response to the query, wherein the operating parameter is associated with at least one of a path for the vehicle to travel, an automatic braking parameter for operating one or more brakes of the vehicle, or a lane change parameter for causing the vehicle to navigate from a first lane to a second lane” incorporates the abstract idea of determining a response to a query into the practical application of adjusting a vehicle’s travel path, braking parameter, or lane change parameter, rather than mere output of the result of the mental process, and thus Independent Claim 15 is found to be eligible under Step 2A Prong Two of the analysis under 35 USC 101.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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, 2, 4 – 8, 10 – 14, 16, 17, 19, & 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to an apparatus for responding to queries by a vehicle occupant (i.e., a machine). Therefore, claim 1 is within at least one of the four statutory categories. Similarly, Claim 16 is directed to a method for responding to queries by the occupant of a vehicle (i.e. a process) and is similarly within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, 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. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
An apparatus for responding to queries, the apparatus comprising:
at least one memory; and at least one processor coupled to the at least one memory and configured to:
obtain a plurality of vehicle to everything (V2X) messages, each V2X message of the plurality of V2X messages comprising a plurality of fields;
obtain a query from an occupant of a vehicle;
process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages, [mental process/step]
wherein the first machine- learning model is trained to determine relevant information based on V2X information and queries; [mental process/step]
process the query and the determined subset of the plurality of the V2X messages [mental process/step] using a second machine-learning model to generate a response to the query; and
use the response to perform at least one of: outputting the response to the occupant of the vehicle using a display of the vehicle, outputting the response using a vocalization system of the vehicle, or adjusting an operating parameter of the vehicle based on the response.
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, “process the query…” in the context of this claim encompasses a looking at data collected regarding a query and V2X information, and forming a simple judgement as to the appropriate query response. While the claim recites the use of a machine learning model to perform the processing, the mere use of a machine learning model encompasses evaluating information under its broadest reasonable interpretation, and therefore is an abstract idea/mental process under said interpretation [See Example 47 Claim 2 Limitation (e) of the 101 Subject Matter Eligibility Guidance]. Further, “process the plurality of V2X messages…” in the context of the claim encompasses evaluating information received (V2X messages) to determine a subset of the plurality of V2X messages comprising at least one field of at least one V2X message of the plurality of V2X messages, which is a mental process of evaluating input data and forming a simple judgement. Finally, the limitation “wherein the first machine- learning model is trained…” in the context of the claim encompasses training a machine-learning model, which is a mathematical calculation under the broadest reasonable interpretation of the claim [See Example 47 Claim 2 Limitation (c) of the 101 Subject Matter Eligibility Guidance]. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). 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” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
An apparatus for responding to queries, [generic linking to technical field, 2106.05(h)] the apparatus comprising: [Apply it, 2106.05(f)]
at least one memory; and at least one processor coupled to the at least one memory and configured to: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
obtain a plurality of vehicle to everything (V2X) messages, each V2X message of the plurality of V2X messages comprising a plurality of fields; [pre-solution activity (data gathering) 2106.05(g)]
obtain a query from an occupant of a vehicle; [pre-solution activity (data gathering) 2106.05(g)]
process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, wherein the subset of the plurality of V2X messages comprises at least one field of at least one V2X message of the plurality of V2X messages,
wherein the first machine- learning model is trained to determine relevant information based on V2X information and queries;
process the query and the determined subset of the plurality of the V2X messages using a second machine-learning model to generate a response to the query; and [particular technological environment or field of use without telling how it is accomplished, Apply it 2106.05(f)]
use the response to perform at least one of: outputting the response to the occupant of the vehicle using a display of the vehicle, outputting the response using a vocalization system of the vehicle, or adjusting an operating parameter of the vehicle based on the response. [insignificant post-solution activity (outputting results of the mental process) 2106.05(g), using generic components Apply it 2106.05(f)]
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 of “at least one memory…,” “obtain a plurality of…,” “obtain a query from…,” “using a second machine-learning model…,” and “use the response to…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the “obtain a plurality of…” and “obtain a query from…” steps are recited at a high level of generality (i.e. as a general means of gathering V2X messages and occupant queries for use in the determination and processing of V2X information to generate a response to the user query), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Further, the “at least one memory…” and associated limitations are recited at a high-level of generality (i.e., as a generic memory and processor performing generic computer functions of processing input data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The limitation “using a second machine-learning model” is also recited at a high level of generality, and amounts to mere linking use of a judicial exception to a particular technological environment or field of use without telling you how it is accomplished. Finally, the limitation “use the response to…” in the context of the claim encompasses, in two of the three embodiments, outputting the results of the determined relevant information through audible or visual means, which is the mere insignificant extra-solution activity of outputting data through the use of components well-known in the art, and is thus not patent-eligible. While the third recitation “adjusting an operating parameter of the vehicle based on the response” recites a practical application of adjusting the vehicle in some way compared to outputting data alone, the practical application is only one possible embodiment of the claimed invention, such that the invention could conceivably be performed only with the output of information through visible or auditory means as an end result, causing the claim as a whole under its broadest reasonable interpretation to encompass the collection, processing, and output of data, which is an abstract idea without significantly more under the broadest reasonable interpretation of the claim(s).
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. see 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 Revised Guidance, representative independent claim 1 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 vehicle controller to perform the processing of data amounts to nothing 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. And as discussed above, the additional limitations of “at least one memory…,” “obtain a plurality of…,” “obtain a query from…,” “using a second machine-learning model…,” and use the response to…,” the examiner submits that these limitations are insignificant extra-solution activities.
Dependent claim(s) 2, 4 – 8, 10 – 14, 17, 19, & 20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically:
Claim 2 recites wherein the at least one V2X message of the determined V2X information comprises a subset of the plurality of fields, which merely narrows the target information of the determination (mental process) step to a specific embodiment, and does not render the claim patent-eligible. Claim 17 recites substantially the same limitations as those found in Claim 2, and is rejected under similar rationale.
Claim 4 recites wherein each V2X message of the plurality of V2X messages is from a respective vehicle of a plurality of vehicles, which merely recites the location the insignificant extra-solution activity of data collection of V2X messages takes place from. Claim 19 recites substantially the same limitations as those found in Claim 4, and is rejected under similar rationale
Claim 5 recites wherein the determined V2X information is based on the distance between a vehicle and the subset of a plurality of vehicles, which is a mental process of determining information (the V2X information) based on the input information of the distance determination, which is a mental process of evaluating data under its broadest reasonable interpretation. Claim 20 recites substantially the same limitations as those found in Claim 5, and is rejected under similar rationale
Claim 6 recites wherein timestamps are associated with V2X messages and used to determine the V2X information, which is an abstract idea of collecting and analyzing data under its broadest reasonable interpretation.
Claim 7 recites wherein the determined V2X information is based on the query, which is a mental process of determining information (the V2X information) based on the input information of a query, which is a mental process of evaluating data under its broadest reasonable interpretation.
Claim 8 recites wherein the determined V2X information is based on the position information of the vehicle, which is a mental process of determining information (the V2X information) based on the input information of a vehicle position, which is a mental process of evaluating data under its broadest reasonable interpretation.
Claim 10 recites wherein the second machine-learning model is trained using contrastive-loss-based optimization, which is a mathematical operation under its broadest reasonable interpretation, and therefore is an abstract idea which does not render the claim patent-eligible.
Claim 11 recites wherein in-context examples are provided to the machine learning model to process the query and determined V2X information, which is a mental process of evaluating data and forming a simple judgement.
Claim 12 recites wherein the response to the query comprises a summary indicative of positions of vehicles and vulnerable road users, which is, under its broadest reasonable interpretation, the mere post-solution output of data, which is insignificant extra-solution activity that does not render the claim patent-eligible [MPEP 2106.05(g)].
Claim 13 recites wherein the apparatus is a computing device of a vehicle, which merely comprises instructions to apply the exception [MPEP 2106.05(f)] with a generic linking to technical field, [MPEP 2106.05(h)] and therefore does not render the claim patent-eligible.
Claim 14 recites wherein the at least one processor is configured to adjust an operating parameter of the vehicle based on the response to the query. While the adjustment of an operating parameter could include a practical application of the abstract idea (see the analysis of Claim 15, below) the adjustment of the operating parameter may indicate mere post-solution display of a response, which is the mere output of data under its broadest reasonable interpretation, and therefore is not patent-eligible.
Therefore, dependent claims 2, 4 – 8, 10 – 14, 17, 19, & 20 are not patent eligible under the same rationale as provided for in the rejection of Independent Claims 1 & 16.
Therefore, claim(s) 1, 2, 4 – 8, 10 – 14, 16, 17, 19, & 20 is/are ineligible under 35 USC §101.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Beaurepaire (US 2022/0122456 A1): Beaurepaire recites an apparatus for responding to a user query regarding the state of the environment around the vehicle, including the exchange of information via V2V/V2X communication, and the receipt of a question from a vehicle user. Information may be identified in relation to the query, and possible answers to the user question may be determined on the basis of the acquired data. The response to the user query may include information relating to other vehicles, such as rationale for said other vehicle’s parking permissions.
Lee (US 2021/0157871 A1): Lee recites a learning processor which collects information regarding a user query and environmental information, which may include V2X information. A machine-learning model may be used to process the query to determine related information to be provided to a user device. Communication hardware may be utilized to acquire the information, including the V2X information used in the evaluation.
Lund (US 2022/0024476 A1): Lund recites a V2X communication system, which utilizes metadata to determine if the data message transmitted from the vehicle is relevant or not, which may be determined in part based on the distance between the own vehicle and the vehicle transmitting a message. The distance may be compared to a threshold, with the message being processed by the receiving device if the distance is within the threshold, and discarded/ignored when outside the threshold.
Stahlin (US 2020/0068405 A1): Stahlin recites a system for filtering V2X messages transmitted between vehicles, including the evaluation of time stamp of the received messages. The time stamp of received messages is compared to a current time, with the V2X messages being discarded when the time stamp of the message exceeds a predefined age.
Tong (US 2023/0252795 A1): Tong recites a neural network training method using contrastive loss methods, which are applied to a neural network to train said network using similarity scores.
Khemka (US 2023/0409615 A1): Khemka recites a fine-tuning approach for a trained model by providing the model with in-context examples from a training set, to enable the model to apply the same reasoning as in the example when answering a question.
Roessler (US 2018/0090009 A1): Roessler recites a method for controlling a device, including the creation of a scene model, including surrounding traffic participants such as vehicles & pedestrians. This may take place responsive to a user input commanding data collection to take place at the device.
Shin (US 2019/0355353 A1): Shin recites a dialogue system for a vehicle, including the receipt of a user query, the determination of the current context of the vehicle, and a determination of a response to the user query is made. The determined response is subsequently output to the user through an interface.
Baghel (US 10,360,797 B2): Baghel recites a V2V communication system, including the reporting of objects detected within a specified distance or particular direction. Multiple vehicles may transmit sensor data, and the sensor data may be filtered or prioritized based on distance.
Oyenan (US 2019/0188328 A1): Oyenan recites an electronic digital assistant, including the receipt of a user query and the execution of an action at the vehicle in response to the user query. Information before the query is spoken may be utilized in order to identify specific context related to the query that may assist in the determination of response.
Maeda (US 11,995,125 B2): Maeda recites a vehicle agent device, including the receipt of vehicle state information and a user question, with inference processing being performed on the received information to infer an intent of the question, and to generate a response. The model is generated through machine learning on a training data set associated with past vehicle states and occupant questions.
Griffin (US 2025/0005632 A1): Griffin recites an in-vehicle voice feedback system, including the binning of vehicle data, indicative of vehicle operation, into a plurality of categories based on voice feedback from a user of the vehicle, said process being performed via the use of machine learning models.
Li (CN 117807199 A): Li recites a dialogue method, specifically with regards to data retrieval and processing. Search and matching methods may take place based on the query, including the extraction of documents and the like with the highest relevance the question posed to the system. Upon the extraction of the text with the highest relevance to the posed question, the relevant text portions are passed to the prompt engineering template in order to obtain an answer result.
Jannach (NPL: A Survey on Conversational Recommender Systems): Jannach recites a survey of software applications that help users find items of interest in cases of information overload. Systems under survey include eliciting current preferences from a user, providing explanations for the suggestions, and iteratively converging on a suggestion on the basis of dialogue and response. Information may be filtered from the recommendation stage that does not meet the preferences of the user(s), with a user preference model being iteratively updated.
Alalo (US 11,884,155 B2): Alalo recites a technique for communicating with a vehicle user, specifically in responding to vehicle occupant queries. The vehicle may determine an answer to the presented question based on sensor data and the like, which may be presented to the occupant in an audible manner. Questions [and answers to such] may include providing information regarding why a vehicle is taking certain actions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661
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