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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claims 1- 13 is to a system (i.e., a machine), claim 14- 19 to a method (i.e., a process), Claim 20 to a computer -readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1, 14 and 20 is (claim 1 being representative):
A vehicle repair intelligence system comprising:
a transceiver configured to receive a vehicle information and user inputs associated with a repair of a vehicle;
a memory configured to store a trained machine model, wherein the trained machine model is trained using a training data that comprises a mapping between vehicle information and user inputs associated with a plurality of vehicles that have historically availed repair services and vehicle part information associated with a plurality of vehicle parts that were replaced in the plurality of vehicles during repair services; and
a processor configured to:
obtain a trigger signal;
obtain the vehicle information and the user inputs associated with the repair of the vehicle responsive to obtaining the trigger signal;
identify a vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and
transmit an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, order, receiving, mapping, obtaining, identifying operational condition of a vehicle and transmitting an order for a vehicle part. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally, and alternatively, the abstract idea steps italicized above relate to transmitting an order to a vehicle part supplier and obtaining a response, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0046] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP 2106.05(f).
Claim 1, 14 and 20 recites the following additional elements: Vehicle repair intelligence, Transceiver, user input, memory, machine model, mapping, processor, trigger signal, vehicle part, vehicle service center.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0022] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 3-13 and 16-19 contain additional steps that further narrow the abstract idea above.
Claim 3 and 16 recites the following additional elements: a vehicle model, a model year, a time in service, an engine type, a transmission type. Applicant has only described generic computing elements in their specification, as seen in {[0024]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 4 recites the following additional elements: vehicle information, vehicle or a server. Applicant has only described generic computing elements in their specification, as seen in {[0024]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 5, 7, 17 recites the following additional elements: vehicle service center, transceiver, user inputs, a computing system. Applicant has only described generic computing elements in their specification, as seen in {[0027]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Claim 6, 8, 9, 10, 11, 12, 13, 18, 19 recites the following additional elements: vehicle service center, transceiver, user inputs, a computing system, trigger signal, a user, order form, vehicle part, transcript, a query, vehicle repair intelligence, auxiliary vehicle parts, vehicle part supplier. Applicant has only described generic computing elements in their specification, as seen in {[0046 and 0051]} of applicant’s specification as filed. This does not integrate the abstract idea into practical application and/or add significantly more. The claim is ineligible. Refer to MPEP 2106.05(F).
Dependent claim 2 and 15, further describes the abstract idea. Claim 2 and 15 are based on the claims describing a mental process of training and determining using diagnostic trouble codes (DTCs). See applicants’ specification {[0024]} for further details. The diagnostic trouble codes (DTCs) are merely generic technology to store vehicle information. The apparatus is not a technical improvement and merely implementing the abstract idea using generic technology. As such additional elements are not significantly more or transformative into a practical application. MPEP 2106.05(F). Therefore, the claims are covered under certain methods of mental process groupings of abstract ideas.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 11-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Horstmann et al; [US 2023/0334438A1] hereafter Horstmann, in view of Starkey et al; [US2024/0203168A1] hereafter Starkey.
As per claim 1, 14 and 20 (Claim 1 being representative):
Horstmann discloses a transceiver;
A vehicle repair intelligence system comprising: a transceiver
{[0022] FIG. 1 shows an exemplary user device 100 for providing a real-time damage estimate and a streamlined repair process using an artificial intelligence (AI) based application according to various exemplary embodiments described herein. The user device 100 includes a processor 105 for executing the AI-based application. The AI-based application may be, in one embodiment, a web-based application hosted on a server and accessed over a network, e.g., a radio access network, via a transceiver 115 or some other communications interface.}
Horstmann discloses;
identify a vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and
{[0062] In 310, the AI system performs an additional image-based assessment for damaged parts to identify certain additional features of these parts. That is, the vehicle parts that were identified as damaged in 305 (e.g., the “normalized” part) can be further analyzed to determine distinguishing features of the part relative to other features than could be found on the part, e.g., whether sensors, fog lamps, etc., are included on the specific part.
[0065] In 325, the AI system orders replacement parts and schedules a repair shop to perform the repairs. This step can include receiving a user selection of these repair process decisions. For example, the AI system can provide to the user a list of repair options via the user device, and the user can select one of the repair options, e.g., the parts vendor or the repair shop to use.}
Horstmann discloses;
transmit an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center.
{[0065] The AI system could automatically notify a parts vendor of the parts order and/or a repair shop of the upcoming job. The AI system can further facilitate the necessary communication between the vehicle owner, the repair shop, the parts vendor, and/or the insurer through all stages of the repair process. The damage assessment can be provided to all interested parties, and additional information can be acquired by the user, the insurer, the parts vendor or the repair shop (e.g., additional images or video) in accordance with the procedures of these entities.}
Horstmann does not explicitly disclose the user input associated with the vehicle, however; Starkey discloses;
configured to receive a vehicle information and user inputs associated with a repair of a vehicle;
[0070] FIGS. 6-8 illustrate portions of the user interface that may allow a user to submit a work order and schedule an action (e.g., a vehicle repair) to resolve a vehicle fault with a vehicle. FIG. 6 shows an example work order in which a user may manually input information about the vehicle to submit the work order. FIG. 7 shows example information about service providers that may be provided to a user.
Starkey discloses the training the model using a historical data, however; Starkey discloses;
a memory configured to store a trained machine model, wherein the trained machine model is trained using a training data that comprises a mapping between vehicle information and user inputs associated with a plurality of vehicles that have historically availed repair services and vehicle part information associated with a plurality of vehicle parts that were replaced in the plurality of vehicles during repair services; and
{[0039] In one or more embodiments, the model may be trained to determine the actions and associated probabilities. The model may specifically be trained using historical data relating to vehicle faults previously identified in other vehicles and the actions that were taken to resolve those vehicle faults. This allows the model to identify actions that were able to resolve prior vehicle faults that may be the same (or similar) type of vehicle fault.}
Starkey discloses the trigger signal
a processor configured to: obtain a trigger signal;
{[0028] Any of the data that is obtained from the vehicle may be monitored to identify when a vehicle fault has occurred. In one or more embodiments, a triggering condition for a fault being identified may include a DTC code being produced by the vehicle indicating a particular type of fault. However, this is not intended to be limiting and any other triggering conditions may also be applicable as well. For example, a vehicle fault may be identified based on recorded audio relating to the vehicle (for example, sounds being produced by the engine, the brakes, etc.). A vehicle fault may also be manually indicated by a user. A vehicle fault may also be identified in any other manner based on any other types of data as well.}
Starkey discloses obtaining the vehicle information
obtain the vehicle information and the user inputs associated with the repair of the vehicle responsive to obtaining the trigger signal;
{[0028] Any of the data that is obtained from the vehicle may be monitored to identify when a vehicle fault has occurred. In one or more embodiments, a triggering condition for a fault being identified may include a DTC code being produced by the vehicle indicating a particular type of fault. However, this is not intended to be limiting and any other triggering conditions may also be applicable as well. For example, a vehicle fault may be identified based on recorded audio relating to the vehicle (for example, sounds being produced by the engine, the brakes, etc.). A vehicle fault may also be manually indicated by a user. A vehicle fault may also be identified in any other manner based on any other types of data as well.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Horstmann’s systems and methods for predictive vehicle repair to include Starkey et al’s a trained machine model, wherein the trained machine model is trained using a training data that comprises a mapping between vehicle information and user inputs associated with a plurality of vehicles that have historically availed repair services and vehicle part information associated with a plurality of vehicle parts that were replaced in the plurality of vehicles during repair services; and a processor configured to: obtain a trigger signal; obtain the vehicle information and the user inputs associated with the repair of the vehicle responsive to obtaining the trigger signal; since Horstmann teaches an intelligence system, a transceiver receiving a vehicle information and user inputs associated with a repair of a vehicle; identifying the vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and transmitting an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center. (See Horstmann [0022, 0062 and 0065]). The combination would have been obvious to one ordinary skill in the art to modify Horstmann to include training a machine model, with the vehicle information, user inputs and historically repair services, DTC code, obtaining a trigger signal and transmitting the required parts to the vehicle service provider. This enables the prediction of the vehicle part(s) for replacement based on vehicle information associated with the vehicle. See Starkey [0028 and 0039].
As per claim 2 and 15, (Similar scope);
Starkey discloses the Diagnostic trouble codes (DTC);
The vehicle repair intelligence system of claim 1, wherein the vehicle information comprises diagnostic trouble codes (DTCs) associated with the vehicle for a predefined historical time duration.
{[0024] The data ingestion process may also involve obtaining data from any number of other data sources as well. For example, historical data relating to vehicle DTCs and actions that were taken to resolve the DTCs may also be obtained. This data may be collected from vehicles over time as the DTCs are identified and the vehicle faults are resolved. The data may also be obtained from data sources, such as databases that may store such information, rather than collecting the data from the vehicles themselves. For example, this data may be obtained from a service facility, one or more vehicles, an original equipment manufacturer (OEM), and/or any other entity that stores such data.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Horstmann’s systems and methods for predictive vehicle repair to include Starkey et al’s vehicle repair intelligence system, wherein the vehicle information comprises diagnostic trouble codes (DTCs) associated with the vehicle for a predefined historical time duration; since Horstmann teaches an intelligence system, a transceiver receiving a vehicle information and user inputs associated with a repair of a vehicle; identifying the vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and transmitting an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center. (See Horstmann [0022, 0062 and 0065]). The combination would have been obvious to one ordinary skill in the art to modify Horstmann to include diagnostic trouble codes (DTCs) to enable the prediction of predefined historical time duration directly from the vehicle. See Starkey [0024].
As per claim 3 and 16; (Similar scope);
Horstmann discloses;
The vehicle repair intelligence system of claim 2, wherein the vehicle information further comprises one or more of: a vehicle model, a model year, a time in service, an engine type, a transmission type, or a mileage.
{[0040] The machine learning models used to determine key features of a normalized part can be agnostic to the specific vehicle (e.g., the make, model and year of the vehicle). The specific vehicle characteristics can be received or determined by the AI system in other ways, e.g., based on some manual input, based on a different machine learning model, or from information stored by the AI system and/or the user device. Thus, the machine learning models used to determine the key features can perform the classification regardless of the vehicle being analyzed. These machine learning models can be trained based on visual characteristics of these features that can be found across multiple different vehicle types.}
As per claim 4;
Horstmann discloses;
The vehicle repair intelligence system of claim 1, wherein the transceiver receives the vehicle information from the vehicle or a server.
{[0022] The user device 100 includes a processor 105 for executing the AI-based application. The AI-based application may be, in one embodiment, a web-based application hosted on a server and accessed over a network, e.g., a radio access network, via a transceiver 115 or some other communications interface.}
As per claim 5;
Horstmann discloses;
The vehicle repair intelligence system of claim 1, wherein the transceiver receives the user inputs from a computing system associated with the vehicle service center.
{[0065] This step can include receiving a user selection of these repair process decisions. For example, the AI system can provide to the user a list of repair options via the user device, and the user can select one of the repair options, e.g., the parts vendor or the repair shop to use. The AI system could automatically notify a parts vendor of the parts order and/or a repair shop of the upcoming job. The AI system can further facilitate the necessary communication between the vehicle owner, the repair shop, the parts vendor, and/or the insurer through all stages of the repair process. The damage assessment can be provided to all interested parties, and additional information can be acquired by the user, the insurer, the parts vendor or the repair shop (e.g., additional images or video) in accordance with the procedures of these entities.}
As per claim 6;
Horstmann discloses;
The vehicle repair intelligence system of claim 5, wherein the transceiver is further configured to receive the trigger signal from the computing system associated with the vehicle service center.
{[0027] In some exemplary embodiments, a determination by the AI that the vehicle is totaled may trigger an action by the insurer. For example, the likelihood of this internal damage could be communicated to a user, such as a repair shop so they are aware that the existence of this damage should be further inspected. In another example, this may trigger the insurer to immediately send an adjuster to visually inspect the vehicle to confirm the AI determination so that no additional time or expense is incurred for the vehicle, trigger a salvage operation by the insurer, notify the vehicle owner that a vehicle replacement process has been triggered, etc.}
As per claim 7;
Horstmann discloses;
The vehicle repair intelligence system of claim 1, wherein the transceiver receives the user inputs from a user device.
{[0022] FIG. 1 shows an exemplary user device 100 for providing a real-time damage estimate and a streamlined repair process using an artificial intelligence (AI) based application according to various exemplary embodiments described herein. The user device 100 includes a processor 105 for executing the AI-based application. The AI-based application may be, in one embodiment, a web-based application hosted on a server and accessed over a network, e.g., a radio access network, via a transceiver 115 or some other communications interface.
[0040] The machine learning models used to determine key features of a normalized part can be agnostic to the specific vehicle (e.g., the make, model and year of the vehicle). The specific vehicle characteristics can be received or determined by the AI system in other ways, e.g., based on some manual input, based on a different machine learning model, or from information stored by the AI system and/or the user device. Thus, the machine learning models used to determine the key features can perform the classification regardless of the vehicle being analyzed. These machine learning models can be trained based on visual characteristics of these features that can be found across multiple different vehicle types.}
As per claim 11 and 19; (Similar scope);
Horstmann discloses;
The vehicle repair intelligence system of claim 1, wherein the processor is further configured to: transmit a query to a computing system associated with the vehicle service center enquiring an availability status of the vehicle part at the vehicle service center, responsive to identifying the vehicle part;
{[0063] In 315, the AI system identifies the availability and delivery time of replacement parts. As described above, the AI system can use the damaged part feature determination of 310 in association with make/model/year information of the damaged vehicle to identify available parts matching the damaged parts. A parts procurement service can provide information for the availability of the replacement part from various vendors integrated with the service, and an estimated time of delivery for the replacement part from one or more vendors can be determined.}
Horstmann does not discloses the availability of the vehicle part, however, Starkey discloses;
obtain a response from the computing system indicating that the vehicle part is not available at the vehicle service center, responsive to transmitting the query; and
{[0051] A further example criteria may include the availability of vehicle parts that are required to perform the vehicle repair. For example, if a vehicle repair to resolve a vehicle fault requires a mass airflow sensor for a 2014 Honda Civic, the system may determine service facilities that have the part available in the timeframe in which the vehicle repair needs to be performed. The system may not only consider service facilities that have the parts currently available but may also consider an amount of time it may take for a service facility to receive the parts if the parts are not currently available. For example, if a preferred service facility does not currently have the mass airflow sensor but is able to acquire a sensor within a week, then this service facility may still be desirable over a less preferable service facility that has the mass airflow sensor currently available.}
Starkey discloses the transmission of request to the supplier;
transmit the order form to the vehicle part supplier responsive to obtaining the response.
{[0049] Another example criteria may include scheduling availability of any potential service facility. For example, a first service facility may be booked for service repairs through a subsequent week. A second service facility, however, may include one or more available time slots available for booking. Thus, in some instances, the second service facility may be selected over the first service facility given the availability of the second service facility, even if the location of the first service facility is more favorable than the second service facility. Additionally, in some cases, the system may perform communications with the various service facilities to determine if any scheduled service repairs for other vehicles may be rescheduled to accommodate the current vehicle experiencing the vehicle fault.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust Horstmann’s systems and methods for predictive vehicle repair to include Starkey et al’s obtaining a response from the computing system indicating that the vehicle part is not available at the vehicle service center, responsive to transmitting the query; and transmit the order form to the vehicle part supplier responsive to obtaining the response; since Horstmann teaches an intelligence system, a transceiver receiving a vehicle information and user inputs associated with a repair of a vehicle; identifying the vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and transmitting an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center. (See Horstmann [0022, 0062 and 0065]). The combination would have been obvious to one ordinary skill in the art to modify Horstmann to include availability of the vehicle part and transmission of request to the supplier to enable convenience and experience of getting the vehicle serviced/repaired and also ensuring that the vehicle becomes available for the user within a short time duration. See Starkey [0049 and 0051].
As per claim 12; (Similar scope);
Horstmann discloses the additional vehicle part.
The vehicle repair intelligence system of claim 1, wherein the processor is further configured to: determine one or more auxiliary vehicle parts needed to repair the vehicle based on the vehicle information, responsive to identifying the vehicle part; and
{[0005] Other exemplary embodiments are related to a method for when the one of the parts of the vehicle incurred damage indicative of a repair operation for the one of the parts comprising replacement of the one of the parts, identifying, using the machine learning model, additional features of the one of the parts of the vehicle, matching the one of the parts and the additional features to a list of available parts provided by a vendor, determining a replacement part in the list of available parts that corresponds to the one of the parts and ordering the replacement part from the vendor.
[0039] For example, a bumper, a side view mirror or a hood is a normalized part. However, within these normalized part classifications, a specific part can include additional features. For example, for a given vehicle type, a bumper may be a standard bumper without any additional features, a split bumper, a bumper with additional features such as fog lamps, sensors, or other features. In another example, the part can be painted a certain color, can be made of plastic or chrome, etc.}
Horstmann discloses;
transmit the order form associated with the one or more auxiliary vehicle parts to the vehicle part supplier to ship the one or more auxiliary vehicle parts to the vehicle service center.
{[0038] Alternatively, the AI system could automatically order the replacement parts for a vehicle to further streamline the repair system. This ordering of a replacement part can include not just a single part, but additional parts associated with replacement of the damaged part. For example, if replacement of a part requires replacement of a specific bolt, the bolt can be included in the order of the replacement part. The specific parts chosen may be chosen from OEM, aftermarket replacement, salvage or other equivalent parts, and this can be done based on a selection by a user, or automatically based on preferences set by a user.}
As per claim 13;
Horstmann discloses;
The vehicle repair intelligence system of claim 1, wherein the order form comprises a part number of the vehicle part.
{[0048] To accomplish the above, a mapping between the identified vehicle part and the parts vendors may be incorporated or trained into the AI system. To again carry through with the above example of the machine learning models identifying a split bumper as the damaged part. The system may be trained to identify the split bumper based on part descriptions from parts databases/catalogs of various parts vendors. A simple case may be considered where the parts database specifically identifies the part based on make/model/year with the description “split bumper.” However, this may not always be the case. For example, the parts may be identified using different nomenclature, e.g., “bumper type II.” This information may be mapped in the AI system so that the AT system understands that “bumper type II” is a split bumper as identified by the machine learning models. Again, while this mapping has been described with reference to a bumper, it will be understood that the mapping}
Claim(s) 8-9, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Horstmann et al; in view of Starkey et al; in view of Leornard et al, [US 2014/0188999A1], hereafter Leornard.
As per claim 8 and 17 (Similar scope);
Leonard discloses a user scheduling a vehicle appointment;
The vehicle repair intelligence system of claim 1, wherein the processor obtains the trigger signal when a user associated with the vehicle schedules a vehicle repair appointment with the vehicle service center.
{[0068] FIGS. 6A-6J illustrate one embodiment of a new appointment interface that may be displayed in response to selecting the schedule new appointment option 502 of FIG. 5. FIG. 6A illustrates a make an appointment interface 600 that includes a vehicle entry field 602, a choose vehicle option 604, and a continue option 606. The vehicle entry field 602 includes a number of fields to allow a user to provide information regarding a vehicle for which the user would like to schedule an appointment. A make field may display a list of vehicle makes that a user can select from.
[0076] The user can review the information before selecting a submit option 644. Selection of the submit option 644 may cause the appointment details to be sent from the mobile computing device 104 to a vehicle status system 102 to schedule the appointment with a service center management system 114. FIG. 6J illustrates a confirmation 646 on the service option summary interface 638 in response to submission and scheduling of the service appointment. In one embodiment, the confirmation 646 is provided following information from the vehicle status system 102 that the appointment was successfully scheduled.
[0095] A schedule module 208 schedules 1405 a service appointment for a vehicle. The schedule module 208 may schedule 1405 the appointment by receiving appointment information from a mobile computing device 104 and scheduling the service appointment within a calendar of a vehicle service center. In one embodiment, the schedule module 208 may schedule 1405 the service appointment for the vehicle with a service center management system 114. The schedule module 208 may also notify the mobile computing device 104 of the success in scheduling the service appointment.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust the combination of Horstmann and Starkey’s systems and methods for predictive vehicle repair to include Leonard et al’s vehicle repair intelligence system, wherein the processor obtains the trigger signal when a user associated with the vehicle schedules a vehicle repair appointment with the vehicle service center; since Horstmann and Starkey teaches an intelligence system, a transceiver receiving a vehicle information and user inputs associated with a repair of a vehicle; identifying the vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and transmitting an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center. (See Horstmann [0022, 0062 and 0065], Starkey [0049]). The combination would have been obvious to one ordinary skill in the art to modify Horstmann to include obtaining a trigger signal from a user to enable scheduling a vehicle appointment with a service center. See Leonard [0068, 0076, 0095].
As per claim 9 and 18; (Similar scope);
Horstmann discloses
The vehicle repair intelligence system of claim 8, wherein the processor is further configured to: generate the order form responsive to identifying the vehicle part; and
{[0062] In 310, the AI system performs an additional image-based assessment for damaged parts to identify certain additional features of these parts. That is, the vehicle parts that were identified as damaged in 305 (e.g., the “normalized” part) can be further analyzed to determine distinguishing features of the part relative to other features than could be found on the part, e.g., whether sensors, fog lamps, etc., are included on the specific part.
[0063] In 315, the AI system identifies the availability and delivery time of replacement parts. As described above, the AI system can use the damaged part feature determination of 310 in association with make/model/year information of the damaged vehicle to identify available parts matching the damaged parts. A parts procurement service can provide information for the availability of the replacement part from various vendors integrated with the service, and an estimated time of delivery for the replacement part from one or more vendors can be determined.}
Horstmann discloses
transmit the order form a predefined time duration before the vehicle repair appointment.
{[0045] In one exemplary use case, a national repair shop chain branded application that implements the exemplary embodiments could allow a customer to take the video. Based on the information derived from the video, the chain may then direct the customer to the best location (e.g., based on proximity and availability of personnel), pre-order the parts, and inform the customer when to bring the vehicle to the shop. For example, if the vehicle is safely drivable, this process may minimize loss of the vehicle to the customer and reduce the need and length for loaner/rental vehicles.}
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Horstmann et al; in view of Starkey et al; in view of Leornard et al, [US 2014/0188999A1], hereafter Leornard, in further view of Coquillette et al, [US 2021/0027256A1], hereafter Coquillette.
As per claim 10;
Coquillette discloses;
The vehicle repair intelligence system of claim 8, wherein the user inputs comprise a transcript of a conversation between the user and an operator associated with the vehicle service center.
{[0076] When integrated into the overall system in accordance with various embodiments of the present disclosure, several functions are facilitated. A tool known as the “notes feed” is available in the user interface (UI) screen for a repair order, or “job.” The notes feed allows a shop user to capture long-hand notes about a job to be performed on a vehicle, included what a customer says about a problem, or tasks to be accomplished. The notes feed allows the attachment and sharing of electronic documents including photos among shop users. The notes feed also allows a user to share—over web-enabled communication including email and text—a link to the repair order or job, and asynchronously communicate with customers about that repair order or job.
[0077] In various embodiments, the notes feed allows a shop user to build a formal structured recommendation for additional work to be performed, including the reason for the work, an estimate of the time required to perform the work, and the cost of the work. That structured recommendation is then shared with and reviewed by the customer asynchronously over an electronic communication link. The customer may use a web browser interface to accomplish this or alternately an app (application) on a smart phone, or even communicate using text messages.}
Motivation: It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine/modify/adjust the combination of Horstmann, Starkey, and Leonard’s systems and methods for predictive vehicle repair to include Coquillette et al’s vehicle repair intelligence system, wherein the processor obtains the trigger signal when a user associated with the vehicle schedules a vehicle repair appointment with the vehicle service center; since Horstmann, Starkey and Leonard teaches an intelligence system, a transceiver receiving a vehicle information and user inputs associated with a repair of a vehicle; identifying the vehicle part to be replaced in the vehicle based on the vehicle information and the user inputs by executing instructions stored in the trained machine model; and transmitting an order form associated with the vehicle part to a vehicle part supplier to ship the vehicle part to a vehicle service center. (See Horstmann [0022, 0062 and 0065], Starkey [0049], Leonard [0068, 0076, 0095]). The combination would have been obvious to one ordinary skill in the art to modify Horstmann to include obtaining a trigger signal from a user to enable scheduling a vehicle appointment with a service center. See Coquillette [0076-0077].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lavie et al [US2023/0237857A1] which teaches: a system and method for predicting when repair and maintenance needs to be performed on a vehicle. The estimates can be based on one or more of in-vehicle sensor measurements during vehicle usage, external observations such as weather and traffic and road conditions and manually or digitally input maintenance and service reports. The gathered information is compared to information in a database from historical maintenance and service and the resulting damage and costs for those. The information is classified by the type of vehicle and the age and usage of the vehicle. Maintaining and refreshing the information and predictive models in the system is also part of the invention.
R. P. A, S. Panda and S. S. G, "Predictive Maintenance for Two-Wheeler Vehicles Using XGBoost," 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2024, pp. 746-751.
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/VICTOR ESONU/
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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