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 12/8/2025 with respect to the rejections under 35 USC 101 have been fully considered but they are not persuasive.
Applicant submits that the amended claims do not recite "using a machine-learning model" at a high level of generality, but rather recite using two machine-learning models (each of which has been trained to perform a specific task) to perform their respective tasks.
During the interview, Examiner Cardimino compared the pending claims to Claim 2 of Example 47 of the Subject Matter Eligibility Guidelines. In the explanation of why step (d) of Claim 2 of Example 47 is ineligible, the Subject Matter Eligibility Guidelines states
Step (d) recites detecting one or more anomalies in a data set using the trained ANN. The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of "detecting" encompasses mental observations or evaluations, e.g., a computer programmer's mental identification of an anomaly in a data set.
In contrast to Claim 2 of Example 47, currently-amended claim 1 recites "process the plurality of V2X messages using a first machine-learning model to determine a subset of the plurality of V2X messages, ... wherein the first machine-learning model is trained to determine relevant information based on V2X information and queries." A recitation of a machine-learning model generating specific outputs based on specific inputs and based on the training of the machine-learning model is a description of how the machine-learning model operates. An example of training such a machine-learning model to determine relevant information is provided in the Instant Application in paragraphs [0131]-[0133].
Accordingly, Applicant requests withdrawal of the rejection of claim 1-20 under 35 U.S.C. § 101.
Examiner respectfully disagrees. The present amendments, while directed to determining “a subset of the plurality of V2X messages” by a first machine learning model, and “a response to the query” using a second machine-learning model, do not appear to recite substantial structure or sufficient details regarding how the machine learning model operates or how the determinations are made to render the claim patent-eligible.
Applicant contends in arguments that “[a] recitation of a machine-learning model generating specific outputs based on specific inputs and based on the training of the machine-learning model is a description of how the machine-learning model operates,” however comparing the present claim(s) to Example 47 of the Subject Matter Eligibility Guidelines, this argument is not found to be persuasive. Specifically, Example 47 Claim 2 [which was found to be ineligible] recites in part:
(a) receiving, at a computer, continuous training data; (b) discretizing, by the computer, the continuous training data to generate input data; (c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; (d) detecting one or more anomalies in a data set using the trained ANN; (e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data; and (f) outputting the anomaly data from the trained ANN.
The above claim recites “one or more anomalies in a data set” and “anomaly data” as outputs from the machine-learning model, and “continuous training data” used to generate “input data” as inputs. While in Example 47 Claim 2, the specific “training data” is recited at a high level of generality, the recitation of “a plurality of V2X messages” as the input does not appear to describe the operation of the machine learning model, rather only reciting a field of use for the system, which does not render the claim patent-eligible under MPEP 2106.05(h).
Thus, for at least the reasons set forth above, as well as those set forth below in the respective section, Examiner maintains their rejections of Independent Claims 1 & 16 under 35 USC 101. Dependent Claims 2, 4 – 8, 10 – 14, 17, 19, & 20 are similarly rejected under 35 USC 101, for at least the reasons set forth below, as well as based on their dependence on a base claim rejected under 35 USC 101.
Allowable Subject Matter
Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: With respect to Dependent Claim 15, the claim recites at least the following limitations:
The apparatus of claim 14, 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.
Claim 14, upon which Claim 15 depends, further reciting:
The apparatus of claim 13, wherein the at least one processor is configured to adjust an operating parameter of the vehicle based on the response to the query.
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 further recited by Dependent Claim 15 of 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 the Independent claim(s), including Independent Claim 1, are rejected under 35 USC 101 for being directed towards an abstract idea/mental process, the additional limitations of “wherein the at least one processor is configured to adjust an operating parameter of the vehicle based on the response to the query” [Dependent Claim 14] and “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” [Dependent Claim 15] incorporate 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, and thus Dependent Claim 15 is found to be eligible under Step 2A Prong Two of the analysis under 35 USC 101.
Thus, Dependent Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Objections
Claims 5 & 6 objected to because of the following informalities: The phrase "wherein the at least one processor is configured to determine subset of the plurality..." found in each claim is grammatically incorrect. Appropriate correction is required.
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 10 & 11 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 10 recites the limitation "the machine-learning model." There is insufficient antecedent basis for this limitation in the Claim, as Claim 1, upon which Claim 10 relies, refers to two machine learning models, a “first machine-learning model” configured to determine a subset of V2X messages, and a “second machine-learning model” configured to generate a response to the query. As multiple machine-learning models exist, it is unclear what antecedent machine learning model “the machine-learning model” is intended to indicate, and thus Claim 10 is rejected under 35 USC 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention.
Claim 11 recites the limitation "the LLM.” There is insufficient antecedent basis for this limitation in the claim, as no LLM has been introduced for the limitation to refer to. While Examiner notes that an LLM in the context of the specification indicates a “large language model,” which is a form of machine-learning model, the earlier introduced machine learning models have not been introduced as LLM(s), and it is further unclear which of the “first machine-learning model” and “second machine-learning model” the LLM would intend to reference in the context of the claim, and thus Claim 11 is rejected under 35 USC 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention.
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; and [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.
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; and
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. [particular technological environment or field of use without telling how it is accomplished, 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…,” and “using a second machine-learning model” 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.
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…,” and “using a second machine-learning model” 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 a 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.
THIS ACTION IS MADE FINAL. 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.
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
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661
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