SYSTEM AND METHOD FOR GENERATION OF COMPLIANCE NOTIIFICATION FOR VEHICLE TRANSACTION

Application No. 18/157,745

• Filed: Jan 20, 2023
• Examiner: LADONI, AHOORA
• Art Unit: 3689
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: Honda Motor Co. Ltd.
• OA Round: 4 (Final)

Office Action

DETAILED ACTION

Election/Restrictions
Claims 15-17 withdrawn from further consideration pursuant to 37 CFR l.142(b) as being drawn to a
nonelected invention II, there being no allowable generic or linking claim. Election was made without
traverse in the reply filed on 12/19/2024.
Applicant's election without traverse of invention I, having claims 1-14 and 18-20 for examination, in the reply filed on 12/19/2024 is acknowledged.

Status of Claims
Claims 1-5, 7-14, 18, and 20 submitted on 09/15/2025 are pending and have been examined. Claims 1, 2, 18, and 20 have been amended. Claims 6 and 19 have been cancelled. Claims 15-17 have been withdrawn.

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 .

Priority
No foreign priority or domestic benefit was claimed by the applicant and the application has been examined with respect to its filing date of 01/20/2023.

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 1-5, 7-14, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.

Step 1
Claims 1-5, 7-14, 18, and 20 are directed to a machine (see MPEP 2106.03).

Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
store a model based on regulation information for a plurality of vehicles at a plurality of first geolocations of a plurality of seller, wherein the regulation information corresponds to a requirement of each of a plurality of features of the plurality of vehicles to operate in one or more geolocations of a plurality of second geolocations of a plurality of buyer; and
receive, first information from a buyer of the plurality of buyer, wherein
the first information corresponds to a vehicle to be purchased for a first geolocation of the buyer,
the plurality of vehicles includes the vehicle, and
the plurality of second geolocations includes the first geolocation;
receive, second information from the plurality of seller, wherein
the second information indicates a level of damage in a set of vehicles to be sold by a plurality of sellers related to the plurality of seller,
the plurality of vehicles includes the set of vehicles,
the level of damage is determined based on at least one of registration information of the set of vehicles or a visual inspection information of the set of vehicles, and
the plurality of first geolocations of the plurality of seller is different from the first geolocation of the buyer;
apply the model on the received first information and the received second information to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation of the buyer, and determine a candidate vehicle having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements;
select a seller from the plurality of seller based on the determination that the candidate vehicle has the lowest compliance-damage score, wherein the seller is associated with the candidate vehicl
generate a first buyer notification including an identification of the candidate vehicle and its compliance-damage scor
transmit, the first buyer notification to an output, and
control the output to render the first buyer notification, wherein ensures vehicle compliance with geolocation-specific regulations by harmonizing heterogeneous seller data and generating compliance scoring that improves efficiency and accuracy of cross-geolocation vehicle transactions.

The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction. Claims 18 recites similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1 and 18 recite an abstract idea.

Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
an electronic device, comprising:
a memory configured to store a neural network model trained based on regulation information for a plurality of vehicles at a plurality of first geolocations of a plurality of seller devices, wherein the regulation information corresponds to a requirement of each of a plurality of features of the plurality of vehicles to operate in one or more geolocations of a plurality of second geolocations of a plurality of buyer devices; and
at least one processor configured to:
receive, through a network, first information from a buyer device of the plurality of buyer devices, wherein
the first information corresponds to a vehicle to be purchased for a first geolocation of the buyer device,
the plurality of vehicles includes the vehicle, and
the plurality of second geolocations includes the first geolocation;
receive, through the network, second information from the plurality of seller devices, wherein
the second information indicates a level of damage in a set of vehicles to be sold by a plurality of sellers related to the plurality of seller devices,
the plurality of vehicles includes the set of vehicles,
the level of damage is determined based on at least one of registration information of the set of vehicles or a visual inspection information of the set of vehicles, and
the plurality of first geolocations of the plurality of seller devices is different from the first geolocation of the buyer device;
apply the trained neural network model on the received first information and the received second information to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation of the buyer device, and determine a candidate vehicle having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements;
select a seller device from the plurality of seller devices based on the determination that the candidate vehicle has the lowest compliance-damage score, wherein the seller device is associated with the candidate vehicl
generate a first buyer notification including an identification of the candidate vehicle and its compliance-damage scor
transmit, through the network, the first buyer notification to an output device, and
control the output device to render the first buyer notification, wherein the electronic device ensures vehicle compliance with geolocation-specific regulations by harmonizing heterogeneous seller data and generating machine-based compliance scoring that improves efficiency and accuracy of cross-geolocation vehicle transactions.

Claim 18 includes the same additional elements as claim 1.

The additional elements recited in claims 1 and 18 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment of neural network models and devices (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see ¶0016, ¶¶0029-0030, and ¶0048).

As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1 and 18 are directed to an abstract idea.

Step 2B
As noted above, while the recitation of the additional elements in independent claims 1 and 18 are acknowledged, claims 1 and 18 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1 and 18 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1 and 18 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1 and 18 are ineligible.
Dependent claims 2-5, 7-14, and 20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-5, 7-14, and 20 merely further define the abstract limitations of claims 1 and 18 or provide further embellishments of the limitations recited in independent claims 1 and 18. Claims 2-5, 7-14, and 20 do not introduce any further additional elements.
Thus, dependent claims 2-5, 7-14, and 20 are ineligible.
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.

Claim(s) 1, 3, 12, 13, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horen et al. (US 2022/0051304 A1 [previously cited]) in view of Ricci et al. (US 2013/0204493 A1).

Regarding Claim 1, Horen discloses an electronic device, comprising:
a memory configured to store a neural network model trained based on regulation information for a plurality of vehicles at a plurality of first geolocations of a plurality of seller devices, the plurality of vehicles to operate in one or more geolocations of a plurality of second geolocations of a plurality of buyer devices (Figs. 1-2[elements 102, 106, 108], Fig. 3[element 306], Fig. 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner notes that a machine learning algorithm is comparable to a neural network model) ... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers ... Adjusted price recommendations may be made for the different regions. Estimated transportation costs and taxes (Examiner notes that estimated taxes are comparable to regulation information corresponding to a plurality of vehicles) for the regions may also be determined and included in the analysis 404] in view of ¶0051 which discloses storing these components); and
at least one processor configured to (Fig. 1[element 106]; ¶0050):
receive, through a network, first information from a buyer device of the plurality of buyer devices, wherein the first information corresponds to a vehicle to be purchased for a first geolocation of the buyer device (Figs. 3 and 5; ¶0132[At step 504, buyer preferences and inventory information are provided to the matching system 202. The information provided as inputs 402 may be any of desired vehicle type, make, model, inventory, preferred inventory ratios of vehicle types, makes and models, prices, locations, sales times, and preferred sellers.]),
the plurality of vehicles includes the vehicle (Figs. 3 and 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers]), and
the plurality of second geolocations includes the first geolocation (Figs. 3 and 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers]);
receive, through the network, second information from the plurality of seller devices, wherein the second information indicates a level of damage in a set of vehicles to be sold by a plurality of sellers related to the plurality of seller devices (Figs. 2-4; ¶0080[The price may be adjusted based on the inputs 402 to the optimization analysis. For example, the price may be reduced based on the inspection data provided above. In some embodiments, historical data and website scraping may be used to determine average amounts that a price may be reduced for reconditioning, for example, repairing the exemplary engine timing faults. This amount may be subtracted from the price of the vehicle to recommend to the seller] in view of ¶0133[the inputs 402 may be obtained from any sources and in any process described in embodiments above. For example, the inputs 402 may be any inputs listed in reference to the seller input in step 306 above] in further view of ¶0056 which discloses a plurality of sellers),
the plurality of vehicles includes the set of vehicles (Figs. 3 and 4; ¶¶0069-0085),
the plurality of first geolocations of the plurality of seller devices is different from the first geolocation of the buyer device (¶0093[Though the seller does not yet have a buyer because of the remote location of the seller, the vehicle may be expected to be transported a great distance] in further view of ¶0056 which discloses a plurality of sellers; Examiner notes that a remote location is comparable to the location being different);
apply the trained neural network model on the received first information and the received second information of the buyer device, and determine a candidate vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the received information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to received information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data maybe analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0056 which discloses a plurality of sellers);
select a seller device from the plurality of seller devices based on the outcome, wherein the seller device is associated with the candidate vehicle (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a first buyer notification including an identification of the candidate vehicle (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]);
transmit, through the network, the first buyer notification to an output device (Fig. 2[showing output devices]; ¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors]), and
control the output device to render the first buyer notification (¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]),
wherein the electronic device ensures vehicle compliance with regulations by harmonizing heterogeneous seller data and generating compliance-scoring that improves efficiency and accuracy of cross-geolocation vehicle transactions (¶¶0133-0135[At step 506, the matching system obtains inputs 402 for analysis. For example, the inputs 402 may be any inputs listed in reference to the seller input in step 306 above… At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0068[The inputs may be provided by the seller or may be accessed by the matching system 202 in a matching database… the inputs may comprise at least one of… inspection, vehicle history data (such as would be included in a CARFAX™ or AutoCheck® report, any vehicle title issues and vehicle owner history)]; Examiner notes that analyzing input such as inspection and vehicle history data in order to determine “the best” vehicles for a buyer is comparable to the device ensuring vehicle compliance with regulations).
Although Horen discloses a neural network model trained based on regulation information for vehicles, Horen does not explicitly teach wherein the regulation information corresponds to a requirement of each of a plurality of features of the vehicles.
However, Ricci et al., hereinafter, Ricci, teaches regulation information corresponding to requirements of features of vehicles to operate in a geolocation (Fig. 4; ¶0178[In one configuration, the score weight for each non-critical system may be defined dynamically according to the location of the vehicle or other factors. For example, vehicle-use laws may affect how a non-critical system should be weighted (i.e., the stringency of emissions law, noise control law, or other laws in one area). Thus, vehicle-use laws may be provided by an organization, governmental entity, group, individual, and/or combinations thereof. The laws may be stored locally or retrieved from a remotely located storage. The vehicle-in-use laws may be statutes and/or regulations that are enforced by a government entity, such as a city, municipality, county, province, state, country, and the like. These laws may define vehicle, traffic, transportation, and/or safety rules associated with a given geographical region.]).
Although Horen discloses a plurality of vehicles listed for sale by sellers, Horen does not explicitly disclose that the level of damage is determined based on at least one of registration information of the set of vehicles or a visual inspection information of the set of vehicles.
However, Ricci teaches determining level of damage based on a visual inspection of the vehicle (Fig. 28; ¶0333[These lights may have a multitude of meanings that may require further inspection by a mechanic or other qualified individual. In order to interpret and decode the meanings behind a light combination, the user is routinely required to consult the owner's manual, the Internet, or to call the dealer. In some cases, these lights are only maintenance reminders and need not be immediately addressed. However, in other cases, the lights are urgent and require immediate attention]).
Although Horen discloses applying a trained neural network model on information of a buyer and seller device, and determining a candidate vehicle, Horen does not explicitly disclose to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation and determine having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements.
However, Ricci teaches computing a compliance-damage score for vehicles relative to requirements of a geolocation and determine a lowest score representing a minimum relative level of damage while satisfying the regulatory requirements (¶¶0176-0179[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.]).
Although Horen discloses selecting a seller device wherein the seller device is associated with the vehicle, Horen does not explicitly disclose a determination that the candidate vehicle has the lowest compliance-damage score.
However, Ricci teaches a vehicle with the lowest compliance-damage score (¶¶0176-0178[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.] in view of ¶0026).
Although Horen discloses generating a buyer notification, Horen does not explicitly disclose generating a notification including an identification of the vehicle and its compliance-damage score.
However, Ricci teaches an identification of compliance score (¶¶0025-0026[The health check module can perform a check and/or test, in response to an internally generated interrupt and/or request to determine a selected processing module's ability to perform critical and/or non-critical vehicle tasks, functions, and/or operations, assign a score to the selected processing module based on the check and/or test results, and compare the score to one or more thresholds and/or to a score of a different processing module to determine a state of health to determine a state of health of the selected processing module.] in view of ¶0026).
Although Horen discloses ensuring vehicle compliance with regulations, Horen does not explicitly disclose ensuring compliance with geolocation-specific regulations and generating machine-based compliance-scoring.
However, Ricci teaches geolocation specific regulations and generating compliance-scoring of a vehicle (¶¶0176-0179[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.] in view of ¶0163[A vehicle may use its location-based features to determine the appropriate applicable laws and enable or disable certain features to a user]).
The system of Ricci is applicable to the system of Horen as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model, plurality of vehicles listed for sale and determination of a candidate vehicle as disclosed by Horen to include compliance-damage scores and geolocation-specific regulations as taught by Ricci. One of ordinary skill in the art would have been motivated to expand the system of Horen in order to comply with various government regulation mandates (¶0010).

Regarding Claim 3, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the requirement for each of a set of features of the plurality of features for the vehicle is related to at least one of a structural feature requirement of the vehicle or a functional feature requirement of the vehicle, which is required for the vehicle to operate in the first geolocation of the buyer device (Figs. 11-13; ¶0042[Preferably, central database 30 receives information from, and may be accessed by, all components of vehicle history information system 12. The information stored in central database 30 may include, for example, the VIN (which indicates make, model and year); accident information, such as salvage title, junk title, flood damage, fire damage, police accident report and damage disclosure information; mileage information, such as odometer problems and actual mileage listings; title and registration events including government registration, taxi registration and commercial registration; stolen vehicle information; fleet information; emissions and safety inspection information; reliability issue information as discussed hereinbelow; and any other information relevant to the vehicle's history.]; Examiner notes that the aforementioned features are comparable to functional feature requirements of the vehicle).

Regarding Claim 12, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the output device is associated with at least one of the buyer device or the seller device (Fig. 2[elements 204 and 208 show the seller and buyer systems respectively]; ¶0008[generating one or more GUls and sending the one or more GU ls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]).

Regarding Claim 13, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the first buyer notification is at least one of a visual notification, an audible notification, an audio-visual notification, or a tactile notification (Fig. 13[example of a visual notification]; ¶0161[Autopilot may send a buyer a real-time notification on a specific vehicle, tailored to the buyer's preferences, such as discussed further below with regard to FIG. 13.]).

Regarding Claim 18, Horen discloses a buyer device, comprising:
a memory configured to store a neural network model trained based on regulation information for a plurality of vehicles at a plurality of first geolocations of a plurality of seller devices, the plurality of vehicles to operate in one or more geolocations of a plurality of second geolocations of a plurality of buyer devices (Figs. 1-2[elements 102, 106, 108], Fig. 3[element 306], Fig. 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner notes that a machine learning algorithm is comparable to a neural network model) ... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers ... Adjusted price recommendations may be made for the different regions. Estimated transportation costs and taxes (Examiner notes that estimated taxes are comparable to regulation information corresponding to a plurality of vehicles) for the regions may also be determined and included in the analysis 404] in view of ¶0051 which discloses storing these components); and
at least one processor configured to (Fig. 1[element 106]; ¶0050):
receive, through a network, first information from the buyer device of the plurality of buyer devices, wherein the first information corresponds to a vehicle to be purchased for a first geolocation of the buyer device (Figs. 3 and 5; ¶0132[At step 504, buyer preferences and inventory information are provided to the matching system 202. The information provided as inputs 402 may be any of desired vehicle type, make, model, inventory, preferred inventory ratios of vehicle types, makes and models, prices, locations, sales times, and preferred sellers.]),
the plurality of vehicles includes the vehicle (Figs. 3 and 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers]), and
the plurality of second geolocations includes the first geolocation (Figs. 3 and 4; ¶¶0069-0085[At step 306 the matching system 202 uses the inputs to determine vehicle pricing recommendations, buyer recommendations, and vehicle sale strategies. The input data may be analyzed using optimization programs and statistical and machine learning algorithms... statistical and machine learning algorithms may also be used to optimize for the desired output 406. For example, parameters of a neural network may be trained on known historical data to determine the most profitable or quickest time-to-sale ... continuing with step 308, prices may be recommended or adjusted based on market index and/or location of the seller and potential buyers]);
receive, through the network, second information from the plurality of seller devices, wherein the second information indicates a level of damage in a set of vehicles to be sold by a plurality of sellers related to the plurality of seller devices (Figs. 2-4; ¶0080[The price may be adjusted based on the inputs 402 to the optimization analysis. For example, the price may be reduced based on the inspection data provided above. In some embodiments, historical data and website scraping may be used to determine average amounts that a price may be reduced for reconditioning, for example, repairing the exemplary engine timing faults. This amount may be subtracted from the price of the vehicle to recommend to the seller] in view of ¶0133[the inputs 402 may be obtained from any sources and in any process described in embodiments above. For example, the inputs 402 may be any inputs listed in reference to the seller input in step 306 above] in further view of ¶0056 which discloses a plurality of sellers),
the plurality of vehicles includes the set of vehicles (Figs. 3 and 4; ¶¶0069-0085),
the plurality of first geolocations of the plurality of seller devices is different from the first geolocation of the buyer device (¶0093[Though the seller does not yet have a buyer because of the remote location of the seller, the vehicle may be expected to be transported a great distance] in further view of ¶0056 which discloses a plurality of sellers; Examiner notes that a remote location is comparable to the location being different);
apply the trained neural network model on the received first information and the received second information of the buyer device, and determine a candidate vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the received information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to received information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data maybe analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0056 which discloses a plurality of sellers);
select a seller device from the plurality of seller devices based on the outcome for the first geolocation of the buyer devic (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a first buyer notification including an identification of the candidate vehicle (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]);

transmit, through the network, the first buyer notification to an output device (Fig. 2[showing output devices]; ¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors]), and
control the output device to render the first buyer notification (¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]),
wherein the buyer device ensures vehicle compliance with regulations by analyzing heterogenous seller data sources and generating compliance-damage scoring that reduces manual intervention and network processing overhead (¶¶0133-0135[At step 506, the matching system obtains inputs 402 for analysis. For example, the inputs 402 may be any inputs listed in reference to the seller input in step 306 above… At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0068[The inputs may be provided by the seller or may be accessed by the matching system 202 in a matching database… the inputs may comprise at least one of… inspection, vehicle history data (such as would be included in a CARFAX™ or AutoCheck® report, any vehicle title issues and vehicle owner history)]; Examiner notes that analyzing input such as inspection and vehicle history data in order to determine “the best” vehicles for a buyer is comparable to the device ensuring vehicle compliance with regulations).
Although Horen discloses a neural network model trained based on regulation information for vehicles, Horen does not explicitly teach wherein the regulation information corresponds to a requirement of each of a plurality of features of the vehicles.
However, Ricci teaches regulation information corresponding to requirements of features of vehicles to operate in a geolocation (Fig. 4; ¶0178[In one configuration, the score weight for each non-critical system may be defined dynamically according to the location of the vehicle or other factors. For example, vehicle-use laws may affect how a non-critical system should be weighted (i.e., the stringency of emissions law, noise control law, or other laws in one area). Thus, vehicle-use laws may be provided by an organization, governmental entity, group, individual, and/or combinations thereof. The laws may be stored locally or retrieved from a remotely located storage. The vehicle-in-use laws may be statutes and/or regulations that are enforced by a government entity, such as a city, municipality, county, province, state, country, and the like. These laws may define vehicle, traffic, transportation, and/or safety rules associated with a given geographical region.]).
Although Horen discloses a plurality of vehicles listed for sale by sellers, Horen does not explicitly disclose that the level of damage is determined based on at least one of registration information of the set of vehicles or a visual inspection information of the set of vehicles.
However, Ricci teaches determining level of damage based on a visual inspection of the vehicle (Fig. 28; ¶0333[These lights may have a multitude of meanings that may require further inspection by a mechanic or other qualified individual. In order to interpret and decode the meanings behind a light combination, the user is routinely required to consult the owner's manual, the Internet, or to call the dealer. In some cases, these lights are only maintenance reminders and need not be immediately addressed. However, in other cases, the lights are urgent and require immediate attention]).
Although Horen discloses applying a trained neural network model on information of a buyer and seller device, and determining a candidate vehicle, Horen does not explicitly disclose to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation and determine having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements.
However, Ricci teaches computing a compliance-damage score for vehicles relative to requirements of a geolocation and determine a lowest score representing a minimum relative level of damage while satisfying the regulatory requirements (¶¶0176-0179[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.]).
Although Horen discloses selecting a seller device wherein the seller device is associated with the vehicle, Horen does not explicitly disclose a determination that the candidate vehicle has the lowest compliance-damage score.
However, Ricci teaches a vehicle with the lowest compliance-damage score (¶¶0176-0178[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.] in view of ¶0026).
Although Horen discloses generating a buyer notification, Horen does not explicitly disclose generating a notification including an identification of the vehicle and its compliance-damage score.
However, Ricci teaches an identification of compliance score (¶¶0025-0026[The health check module can perform a check and/or test, in response to an internally generated interrupt and/or request to determine a selected processing module's ability to perform critical and/or non-critical vehicle tasks, functions, and/or operations, assign a score to the selected processing module based on the check and/or test results, and compare the score to one or more thresholds and/or to a score of a different processing module to determine a state of health to determine a state of health of the selected processing module.] in view of ¶0026).
Although Horen discloses ensuring vehicle compliance with regulations, Horen does not explicitly disclose ensuring compliance with geolocation-specific regulations.
However, Ricci teaches geolocation specific regulations (¶¶0176-0179[The score may be adjusted according to the level non-criticality of the system. For example, an emissions control unit, while it may be non-critical to vehicle operation, may nonetheless be fairly important so as to comply with environmental regulations; therefore an emissions control unit could be weighted a comparatively high score for passing. In contrast, an entertainment system's failure may not be deemed to be important (except for operator/occupant inconvenience) and may be weighted with a relatively low score for passing… In step 421, the score is tabulated for all non-critical systems and compared to see if it is above a certain threshold. If the score is below the threshold, a hand-off procedure is activated in step 450. For example, if emissions control by the processing module 124 is detected to be failing, causing or potentially causing harmful gas emissions to rise significantly above the legal limit, health check 421 may give a very low score to this non-critical system. Therefore, even if the entertainment system is working perfectly, health check may still give a score that is below the threshold and hand-off procedure will be activated.] in view of ¶0163[A vehicle may use its location-based features to determine the appropriate applicable laws and enable or disable certain features to a user]).
The system of Ricci is applicable to the system of Horen as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model, plurality of vehicles listed for sale and determination of a candidate vehicle as disclosed by Horen to include compliance-damage scores and geolocation-specific regulations as taught by Ricci. One of ordinary skill in the art would have been motivated to expand the system of Horen in order to comply with various government regulation mandates (¶0010).

Regarding Claim 20, Horen in view of Ricci teaches the buyer device according to claim 18, Horen further discloses buyer device, and wherein a vehicle is determined by the neural network model (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above. Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above. The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer]).
Although Horen discloses a buyer device and determining a target vehicle by a neural network model computing information, Horen does not explicitly disclose wherein the requirement for each of a set of features of the plurality of features for the vehicle is related to at least one of a structural feature requirement of the vehicle or a functional feature requirement of the vehicle, which is required for the vehicle to operate in the first geolocation of the user, and wherein compliance is determined by computing feature-specific compliance scores.
However, Ricci teaches having requirement for features of a vehicle related to structural or functional features of a vehicle which are required for the vehicle to operate in a geolocation (¶0178[In one configuration, the score weight for each non-critical system may be defined dynamically according to the location of the vehicle or other factors. For example, vehicle-use laws may affect how a non-critical system should be weighted (i.e., the stringency of emissions law, noise control law, or other laws in one area). Thus, vehicle-use laws may be provided by an organization, governmental entity, group, individual, and/or combinations thereof. The laws may be stored locally or retrieved from a remotely located storage. The vehicle-in-use laws may be statutes and/or regulations that are enforced by a government entity, such as a city, municipality, county, province, state, country, and the like. These laws may define vehicle, traffic, transportation, and/or safety rules associated with a given geographical region. An exemplary vehicle-in-use law governs texting, cellular phone use, and video availability to the operator when the car is in motion and the like). The laws may be updated from time to time to, among other things, account for changes in the laws. Thus, a first task, operation, or function may be critical in a first geographic location but noncritical in a different second geographic location.]).
The system of Ricci is applicable to the system of Horen as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model, plurality of vehicles listed for sale and determination of a candidate vehicle as disclosed by Horen to include compliance-damage scores and geolocation-specific regulations as taught by Ricci. One of ordinary skill in the art would have been motivated to expand the system of Horen in order to comply with various government regulation mandates (¶0010).

Claim(s) 2, 4, 5, 7, 8, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horen in view of Ricci in further view of Hecklinger et al. (US 2006/0178793 A1 [previously cited]).

Regarding Claim 2, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein
the first information further corresponds to a first feature of the plurality of features of the vehicle to be purchased (Fig. 5; ¶0132), and
the at least one processor is further configured to:
apply the trained neural network model on the received first information and the received second information to compute a score (Fig. 3 and 5; ¶0147[The feedback may be incorporated into the analysis and provided a weight against the other inputs. A final score for each seller may be determined and presented to the buyer or automatic bids may be generated based on the seller scores.] in view of ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to information). The buyer preferences may be inputs 402 and set constraints (Examiner notes buyer constraints are comparable to a requirement of the vehicle for the first location) on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer]);
generate a notification based on the application of the trained neural network model (Fig. 13; ¶0193[the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest)]),
wherein the generated notification indicates vehicles of interest (Fig. 13; ¶0193[the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest)]); and
control the output device to render the generated notification (Fig. 13; ¶0193[a second example GUI 1300 in which a buyer is presented with limited-time offers for vehicles. As discussed above, the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest)]).
Although Horen discloses applying a neural network model on received first and second information to perform a determination step, Horen in view of Ricci does not explicitly teach determining a feature-specific compliance and determine whether a corresponding feature of the vehicle to be sold complies with the requirement of the first feature of the vehicle for the first geolocation of the buyer device.
However, Hecklinger teaches determining whether the features of a seller’s vehicle comply with the requirements of the geolocation of the buyer (Figs. 11-13; ¶¶0055-0056[an import record may include any information from a reliable source indicating compliance with import standards and preferably an official report from a government agency of the second country. If the system determines in step 212 that no import record, indicating compliance with the import standards of the second country, exists, then the process proceeds to step 214 in FIG. 11 wherein a user interface containing an advisory record is displayed which indicates that the particular vehicle may not meet the import standards of the second country.] in view of ¶0048[ analyzing vehicle history data and following specific logic to ultimately form conclusions regarding a vehicle's compliance with import standards.]; Examiner notes that according to the reference ¶0042, vehicle history information includes vehicle features).
Although Horen discloses generating a notification based on the application of a model, Horen in view of Ricci does not explicitly teach wherein the generated notification indicates whether the corresponding feature of the vehicle to be sold complies with the requirement of the first feature of the vehicle-for the first geolocation of the buyer device.
However, Hecklinger teaches notifying the buyer of the requirement for at least one feature for the geolocation (Figs. 11-13; ¶¶0056-0057[The advisory record, indicated generally at 215, is a gray market vehicle alert suggesting that the vehicle may not have been properly imported. The alert also notifies the user that the vehicle may not comply with the second country, i.e. U.S., safety and emissions standards, the odometer may not reflect accurate mileage after being converted to miles, and the manufacturer warranty may be invalid… FIG. 13 illustrates a user interface similar to FIG. 12 but wherein the import advisory notifies the user that the vehicle was inspected and complies with the second country's import standards, i.e. U.S. highway safety standards, as indicated at 217.]).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations and notifying a buyer regarding vehicle feature requirements as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Regarding Claim 4, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the at least one processor is further configured to:
receive third information from the plurality of buyer devices, wherein the third information corresponds to the vehicle to be purchased for the plurality of second geolocations of the plurality of buyer devices (Fig. 5; ¶0132[At step 504, buyer preferences and inventory information are provided to the matching system 202. The information provided as inputs 402 may be any of desired vehicle type, make, model, inventory, preferred inventory ratios of vehicle types, makes and models, prices, locations, sales times, and preferred sellers] in view of ¶0053 which discloses a plurality of buyers at a plurality of geolocations);
apply the trained neural network model on the received third information and the received second information to determine the vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0053 which discloses a plurality of buyers at a plurality of geolocations);
select one or more buyer devices from the plurality of buyer devices based on the application of the neural network model (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a seller notification based on the selection of the one or more buyer devices (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]); and
control the output device to render the generated seller notification (¶0008[generating one or more GUls and sending the one or more GU ls to facilitate the sale between the seller and the at least one buyer, the one or more GU ls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]).
Although Horen discloses applying a neural network model on received information to perform a determination step, Horen in view of Ricci does not explicitly teach determining whether a set of features of the plurality of features of the vehicle to be sold complies with the requirement of the vehicle for the plurality of second geolocations of the plurality of buyer devices.
Although Horen discloses selecting buyer devices based on the application of a model, Horen in view of Ricci does not explicitly teach selecting buyers based on a determination that the set of features of the vehicle to be sold complies with the requirement of the vehicle for the plurality of second geolocations of the plurality of buyer devices.
However, Hecklinger teaches determining whether the features of a seller’s vehicle comply with the requirements of buyer geolocations (Figs. 2 and 11-13; ¶¶0055-0056[an import record may include any information from a reliable source indicating compliance with import standards and preferably an official report from a government agency of the second country. If the system determines in step 212 that no import record, indicating compliance with the import standards of the second country, exists, then the process proceeds to step 214 in FIG. 11 wherein a user interface containing an advisory record is displayed which indicates that the particular vehicle may not meet the import standards of the second country.] in view of ¶0048[ analyzing vehicle history data and following specific logic to ultimately form conclusions regarding a vehicle's compliance with import standards.] and ¶0036[Vehicle history information system 12 may be implemented using a server, personal computer, a portable computer, a thin client, etc. or any combination of such devices. In this regard, vehicle history information system 12 may be a single device at a single location as shown, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, any other cable, or in a wireless manner using radio frequency, infrared, or other technologies.]; Examiner notes that according to the reference ¶0042, vehicle history information includes vehicle features).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Regarding Claim 5, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the at least one processor is further configured to:
receive fourth information from the plurality of seller devices, wherein the fourth information corresponds to a set of features of the plurality of features of each of the set of vehicles to be sold by the plurality of sellers related to the plurality of seller devices (Fig. 3; ¶¶0067-0073[vehicle information may be obtained. The vehicle information may include any one, any combination, or all of vehicle type, make, model, year, historical data associated with the vehicle, or inspection data ... The vehicle information may be stored in the seller database 206 such that it may be accessed by the matching system 202 for determination of pricing and buyer recommendations and promotions ... The BMW dealer posts a description of the vehicle along with vehicle information such as, for example, pictures, vehicle history, current vehicle specifications, vehicle current condition (e.g., inspection information), ownership history, location, city and highway travel, and any other information that may be useful for a buyer and/or for determining a price recommendation] in view of ¶0093[Though the seller does not yet have a buyer because of the remote location of the seller, the vehicle may be expected to be transported a great distance] in further view of ¶0056 which discloses a plurality of sellers);
apply the trained neural network model on the received first information and the received fourth information to determine the vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0056 which discloses a plurality of sellers);
select one or more seller devices from the plurality of seller devices based on the application of the neural network model (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a second buyer notification based on the selection of the one or more seller devices (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]); and
control the output device to render the second buyer notification (¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]).
Although Horen discloses applying a neural network model on received information to perform a determination step, Horen in view of Ricci does not explicitly teach determining whether the set of features of the set of vehicles to be sold complies with the requirement of the vehicle for the first geolocation of the buyer device.
Although Horen discloses selecting buyer devices based on the application of a model, Horen in view of Ricci does not explicitly teach selecting buyers based on a determination that the set of features of the set of vehicles to be sold complies with the requirement of the vehicle for the first geolocation of the buyer device.
However, Hecklinger teaches determining whether the features of a seller’s vehicle comply with the requirements of buyer geolocations (Figs. 2 and 11-13; ¶¶0055-0056[an import record may include any information from a reliable source indicating compliance with import standards and preferably an official report from a government agency of the second country. If the system determines in step 212 that no import record, indicating compliance with the import standards of the second country, exists, then the process proceeds to step 214 in FIG. 11 wherein a user interface containing an advisory record is displayed which indicates that the particular vehicle may not meet the import standards of the second country.] in view of ¶0048[ analyzing vehicle history data and following specific logic to ultimately form conclusions regarding a vehicle's compliance with import standards.] and ¶0036[Vehicle history information system 12 may be implemented using a server, personal computer, a portable computer, a thin client, etc. or any combination of such devices. In this regard, vehicle history information system 12 may be a single device at a single location as shown, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, any other cable, or in a wireless manner using radio frequency, infrared, or other technologies.]; Examiner notes that according to the reference ¶0042, vehicle history information includes vehicle features).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Regarding Claim 7, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the at least one processor is further configured to:
receive fourth information from the plurality of seller devices, wherein the fourth information indicates a service history of a set of vehicles of the plurality of vehicles to be sold by the plurality of sellers related to the plurality of seller devices, and the service history is determined based on at least one of a number of repairs of the set of vehicles or a number of accidents of the set of vehicles (Fig. 4; ¶¶0073-0080[the matching system 202 receives and analyzes the information input by the seller to determine potential pricing option recommendations for the seller of the vehicle. For example, the vehicle information (e.g., maintenance history (Examiner notes maintenance history is comparable to a number of repairs), inspection report (discussed below)) ... The price may be adjusted based on the inputs 402 to the optimization analysis. For example, the price may be reduced based on the inspection data provided above. In some embodiments, historical data and website scraping may be used to determine average amounts that a price may be reduced for reconditioning, for example, repairing the exemplary engine timing faults. This amount may be subtracted from the price of the vehicle to recommend to the seller] in view of ¶0133[the inputs 402 may be obtained from any sources and in any process described in embodiments above. For example, the inputs 402 may be any inputs listed in reference to the seller input in step 306 above] in further view of ¶0056 which discloses a plurality of sellers);
apply the trained neural network model on the received first information and the received fourth information to determine the vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the second information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to first information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0056 which discloses a plurality of sellers);
select one or more seller devices from the plurality of seller devices based on the application of the neural network model (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a second buyer notification based on the selection of the one or more seller devices (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]); and
control the output device to render the second buyer notification (¶0008[generating one or more GUls and sending the one or more GUls to facilitate the sale between the seller and the at least one buyer, the one or more GUls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]).
Although Horen discloses receiving information regarding service history for a plurality of vehicles and applying a model, Horen in view of Ricci does not explicitly teach determining whether the service history related to a set of features of the plurality of features of the set of vehicles complies with the requirement of the vehicle for the first geolocation of the buyer device.
Although Horen discloses selecting buyer devices based on the application of a model, Horen in view of Ricci does not explicitly teach selecting buyers based on a determination that the service history related to the set of features of the set of vehicles complies with the requirement of the vehicle for the first geolocation of the buyer device.
However, Hecklinger teaches determining whether the service history of a seller’s vehicle comply with the requirements of buyer geolocations (Figs. 2 and 11-13; ¶¶0055-0056[an import record may include any information from a reliable source indicating compliance with import standards and preferably an official report from a government agency of the second country. If the system determines in step 212 that no import record, indicating compliance with the import standards of the second country, exists, then the process proceeds to step 214 in FIG. 11 wherein a user interface containing an advisory record is displayed which indicates that the particular vehicle may not meet the import standards of the second country.] in view of ¶0048[ analyzing vehicle history data and following specific logic to ultimately form conclusions regarding a vehicle's compliance with import standards.]; Examiner notes that according to the reference ¶0042, vehicle history information includes vehicle service history).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Regarding Claim 8, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the at least one processor is further configured to:
receive fourth information from the plurality of seller devices, wherein the fourth information corresponds to at least one of a need for sale for a set of vehicles of the plurality of vehicles to be sold by the plurality of sellers related to the plurality of seller devices or a fit for sale certificate received from a regulatory authority for the set of vehicles to be sold (¶0053[the sellers are vehicle dealers that have vehicle inventory that they wish to sell to other dealers rather than placing the vehicle on their lot and selling to the general public. For example, a BMW dealer may sell a BMW and receive a Honda Civic on trade-in. The BMW dealer may decide that the Honda Civic is not consistent with the type of vehicles sold on the BMW lot. Therefore, the seller may provide information indicative of the Honda to the matching system. The matching system may match the Honda Civic to other dealers (referred to as "buyers" below) to sell the Honda to the general public. The matching system may match the seller to the buyer based on location, the seller, the buyer, time (e.g., season, timing trends), market activity, website data, and any other data that may be stored in the matching database] in view of ¶0056 which discloses a plurality of sellers; according to ¶0087 of the applicant's specification, a need for sale may correspond to an intent to sale a vehicle from a seller);
apply the trained neural network model on the received first information and the received fourth information to determine the vehicle (Fig. 3-5; ¶¶0132-0135[At step 506, the matching system obtains inputs 402 for analysis ... the inputs 402 may be any inputs listed in reference to the seller input in step 306 above (Examiner notes seller input is comparable to the second information). Further, the inputs 402 for the buyer may be any buyer preferences listed in step 504 above (Examiner notes buyer input is comparable to first information). The buyer preferences may be inputs 402 and set constraints on any optimization for the determining the outputs 406 for the buyer ... The input data may be analyzed using optimization programs and statistical and machine learning algorithms (Examiner further notes that a machine learning algorithm is comparable to a neural network model) ... At step 508, the matching system 202 analyzes the inputs 402 and determines the best vehicles for the buyer] in view of ¶0056 which discloses a plurality of sellers);
select one or more seller devices from the plurality of seller devices based on the application of the neural network model (Fig. 4; ¶0147[The buyer may be matched with any seller based on any one or a combination of the inputs 402]; matching a buyer with a seller is comparable to selecting a buyer and a seller);
generate a second buyer notification based on the selection of the one or more seller devices (¶0183[the analysis for seller (including the sale price) and the analysis for buyers (including the purchase price) are compared in order to select at least one buyer to purchase the vehicle. At 1112, the system may optionally send real-time notifications to one or both of the buyer or the seller regarding the sale] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]); and
control the output device to render the second buyer notification (¶0008[generating one or more GUls and sending the one or more GU ls to facilitate the sale between the seller and the at least one buyer, the one or more GU ls comprising notification including an indication of a plurality of attributes of the vehicle and an indication of the associated one or more vehicle factors] in view of ¶0028[the current technology can include a variety of combinations and/or integrations of the embodiments described herein.]).
Although Horen discloses receiving information regarding service history for a plurality of vehicles and applying a model, Horen in view of Ricci does not explicitly teach determining whether the fourth information for the set of vehicles to be sold complies with the requirement of the vehicle for the first geolocation of the buyer device.
Although Horen discloses selecting buyer devices based on the application of a model, Horen in view of Ricci does not explicitly teach selecting buyers based on a determination that the fourth information for the set of vehicles to be sold complies with the requirement of the vehicle for the first geolocation of the buyer device.
However, Hecklinger teaches determining whether the features of a seller’s vehicle comply with the requirements of buyer geolocations (Figs. 2 and 11-13; ¶¶0055-0056[an import record may include any information from a reliable source indicating compliance with import standards and preferably an official report from a government agency of the second country. If the system determines in step 212 that no import record, indicating compliance with the import standards of the second country, exists, then the process proceeds to step 214 in FIG. 11 wherein a user interface containing an advisory record is displayed which indicates that the particular vehicle may not meet the import standards of the second country.] in view of ¶0048[ analyzing vehicle history data and following specific logic to ultimately form conclusions regarding a vehicle's compliance with import standards.] and ¶0036[Vehicle history information system 12 may be implemented using a server, personal computer, a portable computer, a thin client, etc. or any combination of such devices. In this regard, vehicle history information system 12 may be a single device at a single location as shown, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, any other cable, or in a wireless manner using radio frequency, infrared, or other technologies.]; Examiner notes that according to the reference ¶0042, vehicle history information includes vehicle features).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Regarding Claim 9, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the first buyer notification indicates information associated with the vehicle to be sold (Fig. 13; ¶0193[the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest]).
Although Horen discloses a notification that indicates vehicles of interest, Horen in view of Ricci does not explicitly teach wherein the notification indicates information associated with a compliance of the vehicle with the requirement of the vehicle for the first geolocation of the buyer device.
However, Hecklinger teaches notifying the buyer of the requirement for at least one feature for the geolocation (Figs. 11-13; ¶¶0056-0057[The advisory record, indicated generally at 215, is a gray market vehicle alert suggesting that the vehicle may not have been properly imported. The alert also notifies the user that the vehicle may not comply with the second country, i.e. U.S., safety and emissions standards, the odometer may not reflect accurate mileage after being converted to miles, and the manufacturer warranty may be invalid… FIG. 13 illustrates a user interface similar to FIG. 12 but wherein the import advisory notifies the user that the vehicle was inspected and complies with the second country's import standards, i.e. U.S. highway safety standards, as indicated at 217.]).
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations and notifying a buyer regarding vehicle feature requirements as taught by Hecklinger. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a system and method capable of efficiently and effectively determining whether a particular vehicle is subject to an open recall and/or has passed import inspection (¶0008).

Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horen in view of Ricci in further view of Shehata et al. (US 2018/0211299 A1 [previously cited]).

Regarding Claim 10, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein the first buyer notification corresponds to information associated with vehicles of interest (Fig. 13; ¶0193[the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest]).
Although Horen discloses a notification that indicates information associated with vehicles of interest, Horen in view of Ricci does not explicitly teach information associated with a nearest regulatory authority at the first geolocation of the buyer device, to regulate the vehicle in accordance with the requirement for the vehicle to be operated at the first geolocation of the buyer device.
However, Shehata et al., hereinafter, Shehata, teaches information associated with a regulatory authority relevant to the location of the buyer (Fig. 2; ¶0048[In step 212, authorization program 110 publishes the causes for the failed compliance check. The authorization program 110 through the analysis of the governing body requirements, laws, procedures, statutes, and the like is able to consolidate the relevant materials, transcribe the materials into a structure and layout which would provide beneficial, helpful, and understandable by the parties, and present the predetermined parties with the report ... the authorization program 110 publishes the causes for the failed compliance check to the customer and the dealership ... the publication provides direct contact to specific governing body departments to allow for quicker clarification of the errors if additional assistance is necessary ... The results may inform the customer or dealership how to correct the errors if in-person meeting with the governing body is required]).
The system of Shehata is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify the notification as taught by Horen in view of Ricci to include information associated with a regulatory authority as taught by Shehata. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a platform structured marking method and system for examining vehicles and buyers' records at the Department of Motor Vehicle (DMV), sharing, collaborating, and updating both, the dealerships and their buyers with any issues preventing the dealership from completing the process of the vehicle registration and the supply of permanent tags (¶0021).

Regarding Claim 11, Horen in view of Ricci in further view of Shehata teaches the electronic device according to claim 10, Horen further discloses wherein the first buyer notification further corresponds to information related to vehicles of interest (Fig. 13; ¶0193[the buyer may be notified regarding vehicles of interest (either by the system gleaning which vehicles may be of interest and/or by the respective buyer input one or more features of vehicles of interest]).
Although Horen discloses a notification that indicates information associated with vehicles of interest, Horen in view of Ricci does not explicitly teach information related to at least one of a cost or a validity period, for the vehicle to meet the requirement at the first geolocation of the buyer device, via the nearest regulatory authority.
However, Shehata teaches information associated with a regulatory authority related to a validity period for a vehicle to meet requirements (¶¶0046-0047[In step 208, authorization program 110 generates a result report. The result report shows that the vehicle and the customer have achieves the minimum requirements for the vehicle to be registered by the governing body. This report is provided to a predetermined set of parties, e.g. the dealership, the customer, or both. In additional embodiments, additional information may be incorporated to provide additional insight the parties which receive the report for future reference, such as, when the next inspection is, condition of systems or parts of the vehicle]; Examiner notes receiving a report indicating when the next inspection is due, is comparable to information related to a validity period).
The system of Shehata is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify the notification as taught by Horen in view of Ricci to include information associated with a regulatory authority as taught by Shehata. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a platform structured marking method and system for examining vehicles and buyers' records at the Department of Motor Vehicle (DMV), sharing, collaborating, and updating both, the dealerships and their buyers with any issues preventing the dealership from completing the process of the vehicle registration and the supply of permanent tags (¶0021).

Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Horen in view of Ricci in further view of Sierocki et al. (US 2019/0172112 A1 [previously cited]).

Regarding Claim 14, Horen in view of Ricci teaches the electronic device according to claim 1, Horen further discloses wherein
the second information is authenticated based on license information of the seller device, and the plurality of sellers includes the seller (¶0063[the "seller" is a representative of a vehicle dealership that may be selling a vehicle to another dealer. The seller may be any person at the dealership that is looking to reduce the number of vehicles in the inventory by selling to other dealers. In some embodiments, the seller may input a dealer number or show dealer certification or a license such that only dealers are allowed to access and sell vehicles by the matching system 202]).
Although Horen discloses information being authenticated based on licensing information of the seller, Horen in view of Ricci does not explicitly teach authentication based on license information of the seller device and registration information of the vehicle to be sold by a seller of the seller device.
However, Sierocki et al., hereinafter, Sierocki teaches authenticating based on information of the seller device and registration of the vehicle to be sold (Fig. 1; ¶0108[a user registers to access the online motor vehicle sales system 10 by entering their personal details (including their name, email address and phone number) into a registration interface. In FIGS. 2A and 2B, the user then enters their account details (including user name, password and whether the user is a registered motor vehicle dealer). In addition, the user must select whether they are registering a new account or whether their registration will be associated with an existing registration for a motor vehicle dealership] in view of ¶0161[The online motor vehicle sales system 10 allows a registered user (in this case a seller 100) to enter a sale offer into the seller's electronic device 101 (computer, mobile telephone, computing tablet etc.). In order to generate the sale offer, the seller 100 must enter the motor vehicle's registration number (or similar identifier) into a sale offer interface. Once this information is entered, the online motor vehicle sales system server 103 consults with (via the internet 102) a vehicle registration server 107 containing an electronic database maintained by an authority (such as a government authority) of motor vehicle registrations. The vehicle registration server 107 may provide information via the internet 102 to the online motor vehicle sales system server 103 including registration details, vehicle identification details (such as a VIN), information regarding whether a security has been registered on the motor vehicle, information regarding whether the vehicle has been reported stolen and the like]).
The system of Sierocki is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify the second information authentication as taught by Horen in view of Ricci to include authentication based on seller information and vehicle registration numbers as taught by Sierocki. One of ordinary skill in the art would have been motivated to expand the system of Horen in view of Ricci in order to provide a motor vehicle sales system that minimized the costs and risks to both buyers and sellers and that allowed buyers to purchase motor vehicles regardless of the location of the vehicle and the buyer (¶0005).

Response to Arguments
Applicant’s arguments on page 12-14 of the remarks filed 09/15/2025, with respect to the previous 35 USC § 112b rejections have been fully considered and are found to be persuasive in light of the claim amendments.
Accordingly, the previous 112b rejections have been withdrawn.

Applicant’s arguments on pages 14-16 of the remarks filed 09/15/2025, with respect to the previous 35 USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on page 14 of the remarks that the amended claims are not directed to methods of organizing human activity enumerated grouping of abstract ideas. Examiner respectfully disagrees. Applying the model on the received first information and the received second information to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation of the buyer, and determine a candidate vehicle having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements, generating a first buyer notification including an identification of the candidate vehicle and its compliance-damage score, and controlling the output to render the first buyer notification are all part of the abstract idea. The mere execution of the abstract idea on generic and high-level components such as a “trained neural network”, buyer and seller “devices”, “networks”, and “machine-based” compliance scoring does not overcome the rejection and does not provide a technical improvement to how computers process data. The following paragraphs of the instant specification describe these components as generic and high-level: ¶0026, ¶¶0030-0034, and ¶0061.
Furthermore, according to the MPEP 2106.04, the question of whether a claim is “directed to” a judicial exception in Step 2A is now evaluated using a two-prong inquiry. Prong One asks if the claim “recites” an abstract idea, law of nature, or natural phenomenon. Under that prong, the mere inclusion of a judicial exception such as a method of organizing human activity in a claim means that the claim “recites” a judicial exception (see MPEP 2106.04 [“The mere inclusion of a judicial exception such as a mathematical formula (which is one of the mathematical concepts identified as an abstract idea in MPEP § 2106.04(a)) in a claim means that the claim "recites" a judicial exception under Step 2A Prong One.”]). Additionally, MPEP 2106.04 instructs examiners to refer to the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2) (i.e., mathematical concepts, certain methods of organizing human activities, and mental processes) in order to identify abstract ideas. As noted above and in the previous office action, the claims recite determining a vehicle for a buyer. This is an abstract idea because it is a concept of business relations which makes it a method of organizing human activity (i.e., one of the groupings of abstract ideas enumerated in MPEP 2106.04(a)(2)).
Applicant further cites instant specification paragraphs ¶0041, ¶¶0062-0064, and ¶0088 to provide support for technical improvement. Transmitting information corresponding to vehicles to be purchased for a geolocation of a buyer as described in ¶0041 is part of the abstract idea and its execution on generic and high-level components such as a device does not overcome the rejection. Furthermore, determining a compliance or requirement for each buyer to purchase a vehicle, based on the requirement for respective geographic location of each buyer of the plurality of buyer in the plurality of geolocations ,determining the compliance or requirement for each buyer to purchase a vehicle based on the requirement of each buyer or the buyer of the plurality of buyer, determining the compliance or requirement for each seller to sell the vehicle based on the requirement for respective geographic location of each seller or the seller of the plurality of seller and geographic locations of buyers in the plurality of geolocations, determining the compliance for each seller to sell the vehicle based on the requirement of each seller or the seller of the plurality of seller as described in ¶¶0062-0064 are all part of the abstract idea and their mere execution on generic components such as buyer and seller “devices” does not overcome the rejection and does not provide a technical improvement in computing technology. Similarly, receiving information from sellers, validating each seller and determining a potential vehicle that has higher number of features meeting the regulations of the geolocation of the buyer or a minimum level of damage or the vehicle with an increased service life from the set of vehicles of the sellers is part of the abstract idea and the application of the abstract idea on generic and high-level computer components does not overcome the rejection. Furthermore, claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate the judicial exception into a practical application or provide an inventive concept, refer to the MPEP 2106.05(f)(2). The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond linking the use of the judicial exception to a particular technological environment.
Applicant argues on page 15 of the remarks that the amended claims integrate the idea into a practical application. Examiner respectfully disagrees. Harmonizing heterogeneous seller data, generating compliance-damage scores that quantify vehicle condition relative to geolocation-specific regulations are part of the abstract idea. Applying the abstract idea on generic and high-level components such as a “trained neural network” does not overcome the rejection or provide an improvement. As previously noted, claiming the improved speed or efficiency inherent with applying the abstract idea on a computer does not integrate the judicial exception into a practical application or provide an inventive concept, refer to the MPEP 2106.05(f)(2).
Accordingly, Examiner maintains that the invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.

Applicant’s arguments on pages 16-21 of the remarks filed 09/15/2025, with respect to the previous 35 USC § 103 rejections have been fully considered and are mostly moot in view of the new 103 rejection of the amended claims.
Applicant argues on page 16 of the remarks that the cited references Horen and Hecklinger do not teach the newly added feature of “apply the trained neural network model on the received first information and the received second information to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation of the buyer device, and determine a candidate vehicle having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements,” as recited in amended claim 1. Examiner respectfully disagrees. Paragraphs 0132 to 0135 of Horen describe a system that obtains inputs that are used for performing an analysis. The inputs obtained are described as being in reference to seller and buyer inputs. These inputs are comparable to received information from buyer and seller devices. Furthermore, Horen discloses that the input data are analyzed using machine learning algorithms in order to determine a target vehicle, see paragraphs 0132 to 0135. Examiner notes that a machine learning algorithm is comparable to a trained neural network model. Examiner further notes that applying the machine learning algorithm to inputs in order to determine a vehicle is comparable to applying a trained neural network model on received first and second information of the buyer device and determine a candidate vehicle.
Horen does not explicitly disclose applying the model “to compute a compliance-damage score for each of the plurality of vehicles relative to the requirements of the first geolocation” and “having a lowest compliance-damage score, wherein the lowest compliance-damage score represents a minimum relative level of damage while still satisfying the regulatory requirements.” However, the newly cited reference Ricci teaches computing a compliance-damage score for vehicles relative to requirements of a geolocation and determining a lowest score representing a minimum relative level of damage while satisfying regulatory requirements. Paragraphs 0176 to 0179 of Ricci describe generating scores for features of a vehicle. Ricci further describes in the same paragraphs that the scores generated are relative to the local rules and requirements of the geolocation in which the vehicle is residing in. Examiner notes that the aforementioned process is comparable to computing a compliance damage score for vehicles relative to the requirements of the first geolocation. Furthermore, paragraphs 0176 to 0179 of Ricci describe that the vehicle components with the lowest scores are compared and determined to be above a passing threshold which is comparable to having a lowest compliance score that represents a minimum relative level of damage while satisfying the regulatory requirements.
The system of Ricci is applicable to the system of Horen as they share characteristics and capabilities, namely, they are both targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network model, plurality of vehicles listed for sale and determination of a candidate vehicle as disclosed by Horen to include compliance-damage scores and geolocation-specific regulations as taught by Ricci.
Applicant argues on page 18 of the remarks that the cited references Horen and Hecklinger do not teach the newly added feature of “computing a feature-specific compliance score” and “determining whether a corresponding feature complies with a structural or functional requirement” as recited in amended claim 2. Examiner respectfully disagrees. As noted previously, Paragraphs 0132 to 0135 of Horen describe a system that obtains inputs that are used for performing an analysis using a machine learning algorithm. Furthermore, paragraph 0147 of Horen discloses computing a score for sellers.
Horen does not explicitly disclose computing a “feature-specific compliance” score and “determining whether a corresponding feature complies with structural or functional requirement”. However, reference Hecklinger teaches an import record that includes information indicating compliance with import standards from a government agency. Furthermore, Hecklinger describes that the system analyzes the vehicle history data as part of the compliance check, see paragraphs 0048 to 0056. Paragraph 0042 of Hecklinger states that vehicle history information includes odometer problems, emissions and safety inspection, flood damage, fire damage, and reliability issues which are comparable to information corresponding to feature-specific compliance relating to structural or functional requirements of a vehicle.
Applicant argues on page 18 of the remarks that the cited references Horen and Hecklinger do not teach requirements specific to structural and functional features of vehicles in relation to compliance determinations as recited in claim 3 and amended claim 20. Examiner respectfully disagrees. Paragraph 0042 of Horen describes vehicle information related to the VIN (which indicates make, model and year); accident information, such as salvage title, junk title, flood damage, fire damage, police accident report and damage disclosure information; mileage information, such as odometer problems and actual mileage listings; title and registration events including government registration, taxi registration and commercial registration; stolen vehicle information; fleet information; emissions and safety inspection information; and reliability issue information. Examiner notes that the aforementioned features are comparable to structural and functional features of a vehicle in relation to compliance. Furthermore, Hecklinger describes vehicle information requirements such as the VIN (which indicates make, model and year); accident information, such as salvage title, junk title, flood damage, fire damage, police accident report and damage disclosure information; mileage information, such as odometer problems and actual mileage listings; title and registration events including government registration, taxi registration and commercial registration; stolen vehicle information; fleet information; emissions and safety inspection information; and reliability issue information, see paragraph 0042.
Applicant argues on page 18 of the remarks that the cited references Horen and Hecklinger do not teach receiving information from buyer devices and applying the neural network model to evaluate compliance for multiple geolocations of buyers as recited in claim 4. Examiner respectfully disagrees. Paragraph 0132 of Horen describes receiving inputs of desired vehicle type, make, model, inventory, preferred inventory ratios of vehicle types, makes and models, prices, locations, sales times, and preferred sellers. Examiner notes that the described information is comparable to information from buyer devices. Furthermore, Horen describes performing an analysis on the input using a machine learning algorithm in order to determine a target vehicle, see paragraphs 0132 to 0135.
Horen does not explicitly disclose determining whether features of a vehicle to be sold complies with the requirements of a vehicle for geolocation of buyer devices. However, Hecklinger describes an import record with information indicating compliance with import standards and a report from a government agency, see paragraphs 0055 to 0056. Furthermore, Hecklinger describes that vehicle features are analyzed as part of the import process, see paragraphs 0036, 00042, and 0048 to 0056.
Applicant argues on pages 18-19 of the remarks that the cited references Horen and Hecklinger do not teach applying the neural network model to determine compliance of seller-side vehicle feature sets relative to buyer geolocation requirements and generating buyer notifications as recited in claim 5. Examiner respectfully disagrees. Paragraphs 0067 to 0073 of Horen describe obtaining vehicle information such as make, model, year, historical data, or inspection data from seller devices. Furthermore, Horen describes that the information obtained is analyzed using a machine learning algorithm in order to determine a vehicle, see paragraphs 0132 to 0135. Examiner notes that the aforementioned process is comparable to applying a neural network model to determine a vehicle after information regarding features of vehicles is received from sellers. Furthermore, paragraph 0161 of Horen describes that a real-time notification on a vehicle is sent to a buyer and Fig. 13 shows an example of a visual notification.
Horen does not explicitly disclose determining whether the features of the vehicles to be sold complies with geolocation requirements of a buyer. However, as noted previously, Hecklinger describes an import record with information indicating compliance with import standards and a report from a government agency, see paragraphs 0055 to 0056. Furthermore, Hecklinger describes that vehicle features are analyzed as part of the import process, see paragraphs 0036, 00042, and 0048 to 0056.
Applicant argues on pages 19 of the remarks that the cited references Horen and Hecklinger do not teach evaluating service history information via the neural network model to determine compliance as recited in claim 7. Examiner respectfully disagrees. Horen discloses receiving and analyzing inputs by sellers, see paragraph 0080. The input includes the vehicle information (e.g., maintenance history (Examiner notes maintenance history is comparable to a number of repairs), inspection report), see paragraphs 0073-0080. Furthermore, Horen discloses applying a neural network model in the form of a machine learning algorithm in order to determine a target vehicle, see paragraphs 0132 to 0135.
Horen does not explicitly disclose evaluating service history information related to a set of features of a vehicle in a geolocation. However, Hecklinger teaches an import record that is analyzed during the selling process of a vehicle, see paragraphs 0055-0056. Furthermore, paragraph 0042 of Hecklinger describe that the information analyzed consists of the VIN (which indicates make, model and year); accident information, such as salvage title, junk title, flood damage, fire damage, police accident report and damage disclosure information; mileage information, such as odometer problems and actual mileage listings; title and registration events including government registration, taxi registration and commercial registration; stolen vehicle information; fleet information; emissions and safety inspection information; and reliability issue information. The aforementioned information is comparable to evaluating service history information related to features of a vehicle to be sold in a geolocation.
The system of Hecklinger is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the regulatory information and neural network model as taught by Horen in view of Ricci to include determining compliance of vehicle features at geolocations and notifying a buyer regarding vehicle feature requirements as taught by Hecklinger.
Applicant argues on pages 19 of the remarks that the cited references Horen and Hecklinger do not teach fit for sale certificates or need-for-sale information as part of compliance scoring as recited in claim 8. Examiner respectfully disagrees. As noted previously, paragraphs 0132 to 0135 of Horen describe applying a machine learning algorithm or neural network model to information in order to determine a target vehicle. Furthermore, paragraph 0053 of Horen describes sellers or vehicle dealers who list an inventory of vehicles they wish to sell. Furthermore, Horen explains that the sellers provide information indicative of a vehicle they wish to sell to the matching system which will match the seller to a buyer in the same paragraph. Paragraph 0087 of the instant specification states that a need for sale may correspond to an intent to sale from a seller.
Applicant argues on pages 19 of the remarks that the cited references Horen and Hecklinger do not teach notification types and output device associations as recited in claims 9, 12, and 13. Examiner respectfully disagrees. Paragraphs 0056 to 0057 of Horen describe that a buyer using a buyer device (as seen in Fig. 2) is notified regarding their vehicles of interest which is comparable to a notification through an output device. Elements 204 and 208 of Fig. 2 of Horen show the seller and buyer systems and devices. Furthermore, paragraph 0161 of Horen describes that a real-time notification on a vehicle is sent to a buyer and Fig. 13 shows an example of a visual notification.
Applicant argues on page 20 of the remarks that the cited references do not teach notification corresponding to information associated with a nearest regulatory authority at the buyer’s geolocation as recited in claim 10. Examiner respectfully disagrees. Horen discloses a buyer notification associated with a vehicle of interest and shows a visual example in Fig. 13.
Horen does not explicitly disclose a nearest regulatory authority at the buyer’s geolocation. However, Shehata teaches providing direct contact to a specific governing body department to allow for clarification of errors, which is comparable to providing the user with information regarding a nearest regulatory authority, see paragraph 0048 and Fig. 2.
Furthermore, applicant argues on page 20 of the remarks that the cited references do not teach notifications including information related to at least one of a cost or validity period for compliance as recited in claim 11. Examiner respectfully disagrees. As mentioned previously, Horen discloses generating a buyer notification related to a vehicle of interest, see paragraph 0193 and Fig. 13.
Horen does not explicitly disclose a cost or validity period for compliance. However, Shehata teaches a result report that is provided to a customer or dealership which provides insight into information such as when the next inspection is due which is comparable to a cost or validity period for compliance, see paragraphs 0046 to 0047.
The system of Shehata is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify the notification as taught by Horen in view of Ricci to include information associated with a regulatory authority as taught by Shehata.
Lastly, applicant argues on pages 20-21 of the remarks that the cited references do not teach that the information is authenticated based on license information of the seller device and registration information of the vehicle to be sold as recited in claim 14. Examiner respectfully disagrees. Horen describes specifically verifying and allowing access to sellers who provide a dealer number or provide evidence of a license which is comparable to authenticating based on license information of the seller device, see paragraph 0063.
Horen does not explicitly disclose registration information of the vehicle to be sold. However, Sierocki teaches a system which requires a user or seller to enter vehicle registration information in addition to user account details in order to sell a vehicle, which is comparable to authenticating based on registration information of the vehicle to be sold, see paragraphs 0108 and 0161.
The system of Sierocki is applicable to the system of Horen in view of Ricci as they share characteristics and capabilities, namely, they are all targeted to improving vehicle information processing. It would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to modify the second information authentication as taught by Horen in view of Ricci to include authentication based on seller information and vehicle registration numbers as taught by Sierocki.
Accordingly, references Horen, Hecklinger, Shehata, and Sierocki have been maintained and reference Ricci has been added in view of the amendments.

Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHOORA LADONI whose email is [e-mail] and telephone number is [phone]. The examiner can normally be reached M-F 0900–1700 ET.
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/AHOORA LADONI/Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 11/26/2025

Prosecution Timeline

• Jan 20, 2023: Application Filed
• Jan 29, 2025: Non-Final Rejection mailed — §101, §103
• Feb 26, 2025: Response Filed
• May 14, 2025: Final Rejection mailed — §101, §103
• Jul 09, 2025: Response after Non-Final Action
• Aug 04, 2025: Request for Continued Examination
• Aug 06, 2025: Response after Non-Final Action
• Aug 26, 2025: Non-Final Rejection mailed — §101, §103
• Sep 15, 2025: Response Filed
• Dec 01, 2025: Final Rejection mailed — §101, §103 (current)