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Claims 15-17 withdrawn from further consideration pursuant to 37 CFR l.142(b) as being drawn to a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 Claims 15-17 have been withdrawn.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