SYSTEMS AND METHODS FOR PREFERENCE ELICITATION ON CAR DESIGNS

Systems and methods for providing a design with consumer feedback are provided. The method may include receiving a design within a design environment, wherein the design comprises a plurality of attributes. The method may further include automatically generating, using a computer model, consumer-based feedback regarding at least one attribute of the plurality of attributes. The method may additionally include presenting the consumer-based feedback within the design environment in real-time.

TECHNICAL FIELD

Embodiments described herein generally relate to product design and, more particularly, a tool to validate rather than prescribe designs.

BACKGROUND

Traditionally, product design has been based on limited information provided to the designer, and is often top-down, i.e., they are given design specifications and allowed to design within them. Product designers and marketers are rarely cognizant of the bigger picture of what specific types of consumers would prefer, and thus have certain blind spots in their ability to construct a product that meets consumer needs. As a result, some product ideas are not tightly coupled to consumer preferences and some design teams make decisions absent information on a consumer's preference.

SUMMARY

Embodiments described herein generally relate tools to validate rather than prescribe designs. Embodiments relate to having the designer input a design idea and analyze the design against the specified consumer preferences to determine whether the design is consistent with consumer demands. Embodiments also identify common design deficiencies in a particular designer's work as it relates to customer preferences by using a combination of tools from cognitive science and machine learning.

In one embodiment, a system for providing a design with consumer feedback may include one or more processors and a non-transitory, computer-readable medium storing instructions that, when executed by the one or more processors, causes the one or more processors to receive a design within a design environment, wherein the design comprises a plurality of attributes. The instructions may further cause the one or more processors to automatically generate, using a computer model, consumer-based feedback regarding at least one attribute of the plurality of attributes. The instructions may additionally cause the one or more processors to present the consumer-based feedback within the design environment in real-time.

In another embodiment, a method may include receiving a design within a design environment, wherein the design comprises a plurality of attributes. The method may further include automatically generating, using a computer model, consumer-based feedback regarding at least one attribute of the plurality of attributes. The method may additionally include presenting the consumer-based feedback within the design environment in real-time.

DETAILED DESCRIPTION

Product designers often take multiple factors into consideration when designing a new product, such as a vehicle component. Considerations such as functionality, aesthetic appeal, interoperability, and cost can factor into, for example, which components are ultimately utilized in a vehicle. However, this approach can ignore designs that consumers actually want, and wind up introducing a design that is unpopular with a segment/demographic in the market that is important.

By contrast, taking a more consumer-centric approach before a final design is chosen can result in greater consumer satisfaction with the item under design. This advantageous approach can be implemented in the form of a tool to validate designs, rather than one to prescribe designs. This can not only help designers create designs more in accordance with consumer preferences, but also help designers identify areas of design where they lack the information to create a design in accordance with available consumer data. This, in turn, produces a real-world benefit of encouraging design decisions based on important goals such as meeting consumer needs, streamlining the decision-making and acceptance process, and avoiding pitfalls of errant design decisions.

Embodiments of the present disclosure are directed to systems and methods for preference elicitation on car designs. Particularly, this can be realized using a combination of tools from cognitive science and machine learning, and utilizing generative models that predict outputs based on a previous set of inputs and outputs. Such models can also be reward-based models, which predict an output based on the likelihood of receiving positive reward. In this case, the reward may be approval from the designer or user. Each of these models may provide the necessary generality to cover the behavior or preferences of individuals or entire market segments. For instance, one can draw from reward structures or generative models to effectively learn preferences with varying, desired degrees of specificity or generality. This may draw data from consumers to generate a model of consumer preferences. The model is more precise in areas where there is more consumer data and less precise in areas lacking data to accurately predict consumer preferences. To elicit preferences, the system can generate queries based on those data gaps in the learned model, as well as queries provided by the designer/user and active learning.

Referring now toFIG.1A, an example of a user102creating a design106is schematically illustrated in a perspective view100A. A user102may be, by way of non-limiting example, a designer (graphic designer, engineer, artist, marketer, and the like) and/or any other person capable of creating or updating a design106. A design106may be any suitable representation of an object capable of being displayed, created, and/or edited on a user computer104, such as computer-aided design (CAD) and the like. Designs for any suitable objects (automobile components and the like) may be utilized. A user computer104may be any suitable device capable allowing a user102to work with a design106, such as by way of non-limiting example a desktop, laptop, smartphone, tablet, server, personal digital assistant, wearable computing device, and like. Further examples of computers, such as a user computer104, are discussed in more detail with respect toFIG.5.

Referring now toFIG.1B, an example of design query crowd-sourcing among consumers by the user depicted byFIG.1Ais schematically illustrated in an overhead view100B. The user102utilizing their user computer104is depicted as being in communication with multiple consumers108, each using a corresponding consumer computer110. A consumer108may include any person capable of using a computing device to view, evaluate, and/or provide other feedback regarding a design. A consumer computer110may be any suitable device capable allowing a consumer to view, evaluate, and/or provide other feedback regarding a particular design, such as by way of non-limiting example a desktop, laptop, smartphone, tablet, server, personal digital assistant, wearable computing device, and like. Further examples of computers, such as a consumer computer110, are discussed in more detail with respect toFIG.5.

Any suitable number of users102, user computers104, consumers108, and consumer computers110may be utilized in any suitable configuration. Each consumer108may view and provide feedback regarding a design put forth by the user102. In this embodiment, the user102and each consumer108are located remotely from each other such that each user computer104and consumer computer110communicates over a network, as further discussed herein with respect toFIG.5. Any suitable configuration for communication (local, remote, wired, wireless, and the like) may be utilized to facilitate communication between user computer(s)104and/or consumer computer(s)110.

Referring now toFIG.2, an exemplary consumer-in-the-loop approach flowchart200for validating a design according to elicited preferences is schematically depicted. In this embodiment, a user202utilizes one or more design tools204to pose a design query a system206. The user202may correspond to the user102depicted inFIGS.1A-B. The design tools204may be utilized to create and/or modify the design106depicted inFIG.1A, and may utilize any suitable tools or data to create designs, such as CAD packages, textual descriptions, and the like. The system206may utilize any suitable computing configuration, including by way of non-limiting example, one or more servers, a cloud implementation222, one or more databases224, and/or any other suitable computing and/or networking components.

At block208, the user202may compose and/or select a design. As discussed with regards to the design106inFIG.1A, a design may be for any type of object or component thereof. By way of non-limiting examples, an object may be an automobile, and a component may be a brake pedal in the automobile. At block210, a subject design may be created within the design tools204. For example, the user202may create a subject design of a brake pedal in CAD or any other suitable design tool, provide a textual description of the design, and the like.

At block212, the user202may select or create one or more alternative designs to gather preferences. By way of non-limiting example, if the brake pedal in the subject design is rectangular, an alternative design may be an oval-shaped brake pedal. In some embodiments, the user202may create one or more alternative designs. In other embodiments, the user202may select (rather than create) alternative designs, such that the user may be provided one or more potential alternative designs to choose from when selecting an alternative design. In other embodiments, a user202may be able to create one or more alternative designs as well as select another alternative design from among one or more alternative designs from which to choose.

At block214, a target demographic and/or design query parameters may be entered. A target demographic of consumers be based upon one or more of any suitable criteria (age, gender, income, nationality, locality, income, occupation, type of vehicle owned, and the like). Design query parameters may include, by way of non-limiting example, corpus size (i.e., the total number of consumers from which to form a target demographic) and wait time (i.e., an amount of time until a consumer must provide a design query response to select a design). At block216, a design instance may be created by implementing the subject design created by the user202, along with a quantity N of alternative designs, and the target consumer demographic and design query parameters. The design instance may relate to the entirety of a design (e.g., an entire brake pedal) or certain aspects (e.g., brake pedal shape, material, color, and the like). Continuing with the brake pedal example, a design instance may include a subject design of a brake pedal, one or more alternative designs,

At block218, a design query is composed by the system206based upon the subject model, one or more alternative models, target consumer demographic in formation, and the design query parameters. At block220, a determination may be made regarding whether the design query is well-posed. In this embodiment, a design query is well-posed if it satisfies one or more suitable criteria as would readily be known to one of ordinary skill in the art. As a non-limiting example, a point-biserial analysis can reveal whether one design poses a weak alternative(i.e., an outlier) to the subject design, and thus does not provide much insight of the target demographic in a choice between designs having more similar levels of appeal. For example, a useful range for alternative design consumer preference within a target demographic may be 20-30%. Thus, returning to the brake pedal example, an alternative brake pedal design utilizing a very heavy material may be far less preferable to a lighter material used in the material of the subject brake pedal design. Thus, if 95% of consumers in the target demographic prefer the subject design over the alternative design due to the heaviness of the brake pedal material in the second design, the alternative design may not have been well-posed to provide the user202with much useful insight into target demographic consumer preferences among viable/realistic options.

If the design query is not well-posed (“NO” at block220), then at block230the design query may be rejected or otherwise fail, such that the user202then may then revise/update the design query back at block208. Otherwise, if the design query is well-posed (“YES” at block220), then the design query may be crowdsourced over a quantity (M) of people within the specified target demographic. In this embodiment, people within the specified target demographic may correspond to the consumers108depicted inFIG.1B. The design query may be stored in a database224, which, by way of non-limiting example may correspond to the database518discussed herein with respect toFIG.5. The design query may also be crowdsourced in the cloud222, such that the results may be stored in the database224along with the design query. In this way, the database224may be utilized for model training as discussed in more detail with respect toFIG.4.

The crowdsourced M quantity of responses may be utilized to gather statistics at block226. Continuing with the brake pedal example, statistics regarding the responses among consumers within the target demographic may be gathered, such as an analysis showing how many of the M consumers within the target demographic voted for each design, whether by singular choice voting or rank choice voting. At block228, the statistics may be gathered and be visually or otherwise presented as part of the design tools204and/or the system206. Any suitable representation of the statistics may be utilized, including but not limited to graphs, pie charts, textual descriptions, and the like.

Referring now toFIG.3, an exemplary model-in-the-loop approach flowchart300for validating a design according to elicited preferences is schematically depicted. In this embodiment, a user302utilizes one or more design tools304to pose a design query a system306. The user302may correspond to the user102depicted inFIGS.1A-B. The design tools304may be utilized to create and/or modify the design106depicted inFIG.1A, and may utilize any suitable tools or data to create designs, such as CAD packages, textual descriptions, and the like. The system306may utilize any suitable computing configuration, including by way of non-limiting example, a computer learning model and/or any other suitable computing and/or networking components.

At block208, the user302may compose and/or select a design. As discussed with regards to the design106inFIG.1A, a design may be for any type of object or component thereof. By way of non-limiting examples, an object may be an automobile, and a component may be a brake pedal in the automobile. At block310, a subject design may be created within the design tools304. For example, the user302may create a subject design of a brake pedal in CAD or any other suitable design tool, provide a textual description of the design, and the like.

At block312, the user302may select or create one or more alternative designs to gather preferences. By way of non-limiting example, if the brake pedal in the subject design is rectangular, an alternative design may be an oval-shaped brake pedal. In some embodiments, the user302may create one or more alternative designs. In other embodiments, the user302may select (rather than create) alternative designs, such that the user may be provided one or more potential alternative designs to choose from when selecting an alternative design. In other embodiments, a user302may be able to create one or more alternative designs as well as select another alternative design from among one or more alternative designs from which to choose.

At block314, a target demographic and/or design query parameters may be entered. A target demographic of consumers be based upon one or more of any suitable criteria (age, gender, income, nationality, locality, income, occupation, type of vehicle owned, and the like). Design query parameters may include, by way of non-limiting example, corpus size (i.e., the total number of consumers from which to form a target demographic) and wait time (i.e., an amount of time until a consumer must provide a design query response to select a design). At block316, a design instance may be created by implementing the subject design created by the user302, along with a quantity N of alternative designs, and the target consumer demographic and design query parameters. The design instance may relate to the entirety of a design (e.g., an entire brake pedal) or certain aspects (e.g., brake pedal shape, material, color, and the like). Continuing with the brake pedal example, a design instance may include a subject design of a brake pedal, one or more alternative designs,

At block318, a design query may be composed by the system306based upon the subject model, one or more alternative models, target consumer demographic in formation, and the design query parameters. At block320, the system306may make a determination regarding whether the design query is well-posed. If the design query is not well-posed (“NO” at block320), then at block328the design query is rejected or otherwise fails, such that the user302may then revise/update the design query at block308. Otherwise, if the design query is well-posed (“YES” at block320), then at block a learned model322may be used to provide data regarding predicted design preference within a target consumer demographic, such as where consumer preference data may be sparse with respect to a target demographic that has not been asked about a particular feature before. The trained or learned model322in this embodiment may, for example, be derived from a model previously trained in the model training process. In some embodiments, the learned model322may be updated, during or input between uses, via the exemplary model training process discussed herein regardingFIG.4.

Referring now toFIG.4, an exemplary model training process flowchart400used to receive data from the consumer-in-the-loop approach inFIG.2and to train the model depicted inFIG.3is schematically depicted. A database402may contain a design query, results associated with the design query, and/or crowdsourced preference data relating to responses of consumers within a target demographic in response to the design query. The database402may correspond to the databases224inFIG.2and/or518inFIG.5. The design query, target demographic data, and/or preference data may be provided to one or more functions/models to train the system depicted as206inFIGS.2and306inFIG.3.

The design query, target demographic data, and/or preference data may be provided from the database402to a parametric model404, which may be implemented as an encoder/decoder. More specifically, this parametric model404approach may be used (for example) for CAD designs in a manner that discerns the user's design intent using features and constraints, as is known to one of ordinary skill in the art. The parametric model404allows users to automate repetitive/small,/incremental changes to the CAD design, such as those found in families of products. For example, a parametric model404may be utilized in the design of a brake pedal within a known family of related brake pedals.

Alternatively, the design query, target demographic data, and/or preference data may be provided from the database402to a consumer preference cost function406. This may be implemented with an inverse reinforcement learning function, which may be utilized to incentivize a model by trial and error to naturally learn correct decisions and to pursue a long term reward, as is known to one of ordinary skill in the art.

As another alternative, the design query, target demographic data, and/or preference data may be provided from the database402to a nonparametric model408, which may not conform to a normal distribution, and utilize continuous data rather than discrete values. The non-parametric model may utilize ordinal numbers or other relative data (i.e., data that does not have a value as a fixed discrete number). Thus, a parametric Gaussian process (PGP) may be utilized to encode large amounts of data into a small number of hypothetical data points, as is known to one of ordinary skill in the art. For example, where there are large amounts of consumer data regarding design models, hypothetical data points can be derived to make predictions regarding the outcome of a design query.

If the parametric model404or consumer preference cost function406are utilized to process the design query, target demographic data, and/or preference data, then active learning410may be utilized to generate synthetic design queries. The active learning410may utilize users interacting with low-confidence data to provide output back as input for the model, as is known to one of ordinary skill in the art. Alternatively, if the nonparametric model408is utilized to process the design query, target demographic data, and/or preference data, then Bayesian optimization412may be utilized to optimize the output of the nonparametric model408to generate synthetic design queries to the cloud414. Bayesian optimization412may be used to optimize functions whose operating details are not completely known, as would be understood by one of ordinary skill in the art. For example, the performance of design queries may be modified by Bayesian optimization412based upon beliefs about the behavior the design queries.

Synthetic queries in this embodiment utilize data regarding previous data in the model regarding the design query, target demographic data, and/or preference data to self-generate design queries to be further evaluated. The synthetic queries may be run utilizing the cloud414to elicit crowdsourced preference data for one synthetic design over another. Specifically, M responses from M consumers in the target demographic may be utilized to determine a preference for one design over another. The cloud414implementation may correspond to the cloud depicted in222ofFIG.2and/or the network(s) in514ofFIG.5.

Turning now toFIG.5, a block diagram illustrates an exemplary computing device500, through which embodiments of the disclosure can be implemented. The computing device500described herein is but one example of a suitable computing device and does not suggest any limitation on the scope of any embodiments presented. The computing device500in some embodiments may also be utilized to implement the designer computer104, the consumer computers110, the system206depicted inFIG.2, the system306depicted inFIG.3, and/or any combination thereof. Nothing illustrated or described with respect to the computing device500should be interpreted as being required or as creating any type of dependency with respect to any element or plurality of elements. In various embodiments, the computing device500may include, but need not be limited to, a desktop, laptop, server, client, tablet, smartphone, or any other type of device that can utilize data. In an embodiment, the computing device500includes at least one processor502and memory comprising non-volatile memory508and/or volatile memory510. The computing device500can include one or more displays and/or output devices504such as, for example, monitors, speakers, headphones, projectors, wearable-displays, holographic displays, and/or printers. Output devices504may further include, for example, displays and/or speakers of the designer computer104, the consumer computers110, devices that emit energy (radio, microwave, infrared, visible light, ultraviolet, x-ray and gamma ray), electronic output devices (Wi-Fi, radar, laser, etc.), audio (of any frequency), and the like.

The computing device500may further include one or more input devices506which can include, by way of example, any type of mouse, keyboard, disk/media drive, memory stick/thumb-drive, memory card, pen, touch-input device, biometric scanner, voice/auditory input device, motion-detector, camera, scale, and any device capable of measuring data such as motion data (e.g., an accelerometer, GPS, a magnetometer, a gyroscope, etc.), biometric data blood pressure, pulse, heart rate, perspiration, temperature, voice, facial-recognition, motion/gesture tracking, gaze tracking, iris or other types of eye recognition, hand geometry, oxygen saturation, glucose level, fingerprint, DNA, dental records, weight, or any other suitable type of biometric data, etc.), video/still images, and audio (including human-audible and human-inaudible ultrasonic sound waves). Input devices506may include cameras (with or without audio recording), such as digital and/or analog cameras, still cameras, video cameras, thermal imaging cameras, infrared cameras, cameras with a charge-couple display, night-vision cameras, three-dimensional cameras, webcams, audio recorders, and the like.

The computing device500typically includes non-volatile memory508(e.g., ROM, flash memory, etc.), volatile memory510(e.g., RAM, etc.), or a combination thereof. A network interface512can facilitate communications over a network514with other data source such as a database518via wires, a wide area network, a local area network, a personal area network, a cellular network, a satellite network, and the like. Suitable local area networks may include wired Ethernet and/or wireless technologies such as, for example, wireless fidelity (Wi-Fi). Suitable personal area networks may include wireless technologies such as, for example, IrDA, Bluetooth, Wireless USB, Z-Wave, ZigBee, and/or other near field communication protocols. Suitable personal area networks may similarly include wired computer buses such as, for example, USB and FireWire. Suitable cellular networks may include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM. Network interface512can be communicatively coupled to any device capable of transmitting and/or receiving data via one or more network(s)514. Accordingly, the network interface512can include a communication transceiver for sending and/or receiving any wired or wireless communication. For example, the network interface512may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card, mobile communications hardware, near-field communication hardware, satellite communication hardware and/or any wired or wireless hardware for communicating with other networks and/or devices.

A computer-readable medium516may comprise a plurality of computer readable mediums, each of which may be either a computer readable storage medium or a computer readable signal medium. A computer readable storage medium may reside, for example, within an input device506, non-volatile memory508, volatile memory510, or any combination thereof. A computer readable storage medium can include tangible media that is able to store instructions associated with, or used by, a device or system. A computer readable storage medium includes, by way of example: RAM, ROM, cache, fiber optics, EPROM/Flash memory, CD/DVD/BD-ROM, hard disk drives, solid-state storage, optical or magnetic storage devices, diskettes, electrical connections having a wire, or any combination thereof. A computer readable storage medium may also include, for example, a system or device that is of a magnetic, optical, semiconductor, or electronic type. Computer readable storage media and computer readable signal media are mutually exclusive.

A computer readable signal medium can include any type of computer readable medium that is not a computer readable storage medium and may include, for example, propagated signals taking any number of forms such as optical, electromagnetic, or a combination thereof. A computer readable signal medium may include propagated data signals containing computer readable code, for example, within a carrier wave. Computer readable storage media and computer readable signal media are mutually exclusive.

The computing device500may include one or more network interfaces512to facilitate communication with one or more remote devices, which may include, for example, client and/or server devices. The network interface512may also be described as a communications module, as these terms may be used interchangeably. The database518is depicted as being accessible over the network514and may reside within a server, the cloud, or any other configuration to support being able to remotely access data and store data in the database518.

It should now be understood that embodiments of the present disclosure are directed to systems and methods for providing a design with consumer feedback using consumer-in-the-loop approach or model-in-the-loop embodiments. A user may compose a design and provide alternative designs to give consumers within a target demographic a choice among the designs. In a consumer-in-the-loop embodiment, the design query may be crowdsourced over a quantity of consumers in a target demographic and the resulting data may be used to train the model. In the model-in-the-loop embodiment, the model may be learned over all consumers in a set. In either embodiment, statistics may be gathered pertaining to the generated responses for consumer design preference. In training the model, the computer model may be generated based upon a well-posed design query crowdsourced among a subset of people in a target demographic utilizing a parametric model, nonparametric model, or a consumer preference cost function. The computer model may be further generated by utilizing active learning to generate synthetic queries whose synthetic results are stored in a database and used to further train the model.