Patent Publication Number: US-2021188051-A1

Title: Identifying and mitigating vehicle odors

Description:
TECHNICAL FIELD 
     The disclosure relates generally to identifying and mitigating odors in vehicles. 
     BACKGROUND 
     Odors and fragrances within a vehicle can significantly contribute to or detract from a passenger&#39;s comfort and enjoyment during travel. As automated vehicles and vehicle sharing becomes more common, identification and mitigation of odors may become more challenging and more difficult. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Non-limiting and non-exhaustive implementations of the present disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified. Advantages of the present disclosure will become better understood with regard to the following description and accompanying drawings where: 
         FIG. 1  is a schematic diagram illustrating an operating environment for an odor mitigation system, according to one implementation; 
         FIG. 2  is a schematic block diagram illustrating interconnections between an odor mitigation system and other sensors or data sources, according to one implementation; 
         FIG. 3  is a schematic block diagram illustrating components and interconnections of a mobile communication device, according to one implementation; 
         FIG. 4  is a schematic block diagram illustrating components of an odor mitigation system, according to one implementation; 
         FIG. 5  a schematic flow chart diagram illustrating a method for mitigating an odor or smell, according to one implementation; 
         FIG. 6  a schematic flow chart diagram illustrating a method for processing sensor data and mitigating odors based on passenger preferences, according to one implementation; 
         FIG. 7  is a schematic flow chart diagram illustrating a method for learning a mitigation strategy during attempting mitigation of an odor or smell, according to one implementation; 
         FIG. 8  is a schematic flow chart diagram illustrating a method for determining a user&#39;s preferences with regard to a smell, according to one implementation; 
         FIG. 9  is a schematic flow chart diagram illustrating a method for learning a new mitigation strategy while eliminating an odor and retaining the fragrances, according to one implementation; and 
         FIG. 10  is a schematic flow chart diagram illustrating a method for interaction between an odor mitigation system and a mobile communication device of an occupant, according to one implementation; and 
         FIG. 11  is a schematic block diagram illustrating a computing system, according to one implementation. 
     
    
    
     DETAILED DESCRIPTION 
     Odor control is an important future differentiator between vehicles in all markets. In “mobility as a service” activities where many people are sharing vehicles or using vehicle owned by someone else, odor control becomes even more important and more complex solutions are needed. Strategies for odor control or mitigation may include controlling odor precursors, removing the source of the odor, dilution of the odor, emission of fragrances, biological or chemical transformation to destroy or change odor causing particles or chemicals, chemical binding to remove odor causing particles or chemicals, masking of odors, and/or olfactory desensitization. Vehicles may have passive and active controls for cabin odors such as windows which may be opened or closed, HVAC (heating, ventilation, and air condition) recirculation control, HVAC dehumidification, sources for air fragrances, systems for adding water ions to the air, systems for air filtration and particle/chemical absorbance using antimicrobial materials, absorbents, ultraviolet light, or the like. 
     While some existing odor control systems may be quite complex, Applicants have recognized the need to automate odor control systems and techniques using artificial intelligence and other technologies to make them more effective, easier and less distracting to use. Odor mitigation can be quite complex because there is a lack of models that comprehensively relate psychological responses to odor with the chemistry that produce them. The problem is likely to become even more complex as new sensing systems evolve and more specific mitigation approaches evolve. Furthermore, response to odors can be very specific to individuals and may vary based on cultural, geographic, demographic, or other backgrounds. The response to odors is frequently time dependent. That is odor introduced gradually may cause no response to humans. Furthermore, a cascade or series of odors may be indicative of a particular source or event. 
     At least one embodiment disclosed herein may provide for automatic means of odor control. Other embodiments may provide advice or notify a user of steps that can be taken to perform odor control or mitigation. For example, a driver of a vehicle who is planning to pick up a passenger may be reminded to remove a source of an odor, apply a fragrance, or ventilate the vehicle before picking up the passenger. 
     One of the best odor sensors in a vehicle are the occupants or passengers. Even if cabin sensors do not detect an odor, the occupants may. The complexity of describing an odor may require a spoken dialog system to communicate between an occupant and an odor mitigation system. However, occupants may provide individualized descriptions of odors if no common descriptive language exists. Thus, embodiments may utilize phrases or terms provided by occupants, even unique to a specific occupant, based on occupant responses. 
     In some embodiments, a system may encounter a new odor which does not have a known mitigation strategy, at least for a specific vehicle. If the sensor fingerprint (e.g., odor characteristics as detected by odor sensors) and an occupant confirms the existence of an odor, the system may need to explore mitigation strategies to find one that is effective. For example, if skunk smell is detected it may be best to close the windows as quickly as possible to prevent the odor from entering the vehicle. If it is cigarette smoke, the opposite may be the case and vehicle systems bring fresh air into the cabin. By learning what steps have contributed to odor mitigation previously or in different vehicles, systems may improve and share odor mitigation strategies. 
     Embodiments disclosed herein may include systems, methods, and devices for identifying odors in a vehicle, learning or storing an odor&#39;s chemistry, learning or storing a human language label for the odor, and/or identifying or sharing odor mitigation strategies. In at least one embodiment, a method for odor mitigation may include detecting a known smell using on one or more odor sensors in a vehicle. The method may include determining whether the known smell is agreeable to one or more passengers of the vehicle. The method may include mitigating the known smell using one or more odor control devices if the known smell is not agreeable to the one or more passengers of the vehicle. Embodiments may allow for an odor mitigation system to ensure that a vehicle always smells good. “Good” in this case is a qualitative term that can mean different things to different occupants. For example, at least one embodiment learns an occupants&#39; preferences and removes smells or adds fragrance discriminately based on a history of dialog with the occupants. In some cases, a vehicle or even a mobile device of an occupant may log preferences specific to the vehicle or occupant. 
     At least some embodiments provide for learning capabilities. For example, an odor mitigation system may learn various aspects of odor management. Example aspects of odor management may include how to use odor management devices on a vehicle to control a specific odor, how to interpret sensor data to determine identify odors in the air, and a descriptive language for odors used by occupants in general or for a specific occupant. Training for these aspects may be both collaborative and occur during real world usage. Occupants may have a personal preference database stored on a mobile device that they carry into each vehicle. The vehicle may have a vehicle database that contains rules and adjustments for peculiarities of the individual vehicle because different vehicles may have different geometries, HVAC systems, sensors, mitigation devices, controllers, or the like. 
     An odor mitigation system may implement a spoken dialog system to allow it to interact with the occupants of a vehicle and process input data from vehicle occupants. The odor mitigation system monitors the changes in sensor data or measurements and may look for patterns that would indicate a known smell. If the recommender detects a known smell, it determines whether the individuals in the vehicle consider the smell to be disagreeable (an odor) or agreeable (a fragrance). The odor mitigation system may poll the individual preference databases in each mobile device in the vehicle and determine whether mitigation of the smell is necessary based on a “vote”. 
     If there is a smell in the vehicle that the odor mitigation system is unfamiliar with, a vehicle occupant may report it to the odor mitigation system. The odor mitigation system will try to identify the pattern of sensor measurements that are consistent with the odor. The odor mitigation system may connect to other odor mitigation systems, and if the smell is known, information about it is downloaded from the other odor mitigation system and mitigation begins if necessary. If the smell is unknown even to other odor mitigation system the odor mitigation system may use its resources to learn occupant&#39;s responses to the odor in the vehicle, how it can be identified, and how it can be mitigated. Information about smells can be exchanged using a vehicle-to-vehicle (V2V) or a mobile communication network. 
     The vehicle sensors used to detect smells and provide input to the odor mitigation system may be specific gas sensors, arrays of specific gas sensors, sensors that measure physical properties of the air in the vehicle (e.g., air temperature, pressure, humidity, particulate size or count, etc.). Odor mitigation devices or odor control devices may include the vehicle&#39;s windows, blower, HVAC doors, devices that inject chemicals into the air, generate ions, dehumidify the air, remove chemicals from the air, use static electric charge, emit light (ultraviolet light), move air, and/or devices that modify the source of smells such as anti-microbial filtering devices. 
     For the purposes of this disclosure, smells result from a combination of micro-components of the air that produce an olfactory or other physiological stimulus and a psychological response to occupants. Odors are smells that produce a disagreeable response while fragrances are smells that produce an agreeable response to occupants. 
     Further embodiments and examples will be discussed in relation to the figures below. 
       FIG. 1  is a schematic diagram illustrating an operating environment  100  for an odor mitigation system  102 , according to one embodiment. The operating environment illustrates a vehicle  104  carrying a plurality of occupants. The vehicle  104  includes the odor mitigation system  102  (such as within an in-dash computing system), a plurality of smell sensors  106 , a plurality of odor mitigation devices  108 , and mobile communication devices  110  of the occupants. The odor mitigation system  102  may be in wired or wireless communication with the sensors  106 , mitigation devices  108 , and/or mobile communication devices  110 . The odor mitigation system  102  may also communicate with another vehicle  112  (or vehicles) or any other device directly or via a mobile network tower  114  (e.g., over the Internet)). 
     The sensors  106  may include an electrochemical nose (e-nose) with an array of sensors. An electrochemical nose may include sensors and implement algorithms to identify the presence or combination of chemicals or other attributes of air within the vehicle. The sensors  106  or the electrochemical nose may include one or more of a molecular sensor, chemosensor, gas chromatography sensors or other sensor for smell classification. Examples of specific sensor technologies which may be used include conductive-polymer odor sensors, tin-oxide gas sensors, quartz-crystal micro-balance sensors, capacitive micromachined ultrasonic transducers (CMUT), or the like. 
     The mitigation devices  108  may include any type of device for adding, moving, or removing air from a vehicle interior. Other mitigation devices  108  may include sources for fragrances, chemicals for reacting to chemical components of odors, air filters or purifiers, or the like. For example, window controllers, HVAC systems, filtration systems, air freshener systems, or the like may be examples of mitigation devices  108 . In one embodiment, a mitigation device  108  may include a plurality chemical sources that can be used to selectively emit chemicals or compounds into the air that will react with different odor components or chemicals. For example, one source may emit a first chemical that reacts with a first type of odor causing chemical while a second source may emit a second chemical that reacts with a different type of odor causing chemical. 
     The odor mitigation system  102  may be implemented as a computing system having one or more processors and storage readable media in communication with the processor(s). The odor mitigation system  102  may identify and mitigate odors in the vehicle  104  based on data from the sensors  106 , mobile communication devices  110 , other vehicles  112 , or any other source. The odor mitigation system  102  may maintain a database containing user preferences regarding odors and odor mitigation. In some situations, the odor mitigation system  102  may remove odors or add fragrances based on known user preferences (e.g., preferences stored by the odor mitigation system  102  or provided by the mobile communication devices  110 ). Additionally, the odor mitigation system  102  may communicate with other vehicles or systems to resolve odor problems within the vehicle  104 . 
     In one embodiment, the odor mitigation system  102  is capable of learning how to mitigate odors over time by using odor mitigation devices  108  to control a specific odor, how to interpret sensor inputs to identify odors, and to develop a descriptive language (spoken by human users) for odors. In some situations, a particular odor is identified by a user, but the odor mitigation system  102  is not familiar with mitigating the odor. To mitigate the odor, the odor mitigation system  102  in the specific vehicle communicates with other odor mitigation systems in other vehicles (e.g., vehicle  112 ) to receive odor mitigation information related to the identified odor. The received information is then used to mitigate the odor in the vehicle  104  and the information is stored by the odor mitigation system  102  for future reference. 
       FIG. 2  is a schematic block diagram illustrating interconnections between an odor mitigation system and other sensors or data sources. The odor mitigation system  102  may include a smell cache  202  that stores information about a smell such as the sensor attributes corresponding to the smell, the preferences of one or more users with regard to the smell, human language or terms associated with the smell, and/or mitigation procedures for mitigating or removing the smell (odor). The odor mitigation system  102  may communicate with mobile communication devices  110  of occupants to receive their preferences with regard to a smell. The odor mitigation system  102  may also access an external smell database  204  that is located remotely from the vehicle  104 , such on a server accessible via the Internet. The external smell database may include information specific to the vehicle  104 , occupants of the vehicle, the type of vehicle corresponding to the vehicle  104 , and/or smell data (e.g., preferences) for the general population. The odor mitigation system  102  may also communicate with odor mitigation systems  206  for other vehicles to obtain smell preferences, mitigation procedures, or the like. 
       FIG. 3  is a schematic block diagram illustrating components and interconnections of a mobile communication device  110 , according to one embodiment. For example, the mobile communication device  110  may include a smartphone, tablet, or other computing device with storage, hardware, and/or installed programs or applications (e.g., computer code or instructions) that make up various components of the mobile communication device  110 . A personal smell recommender  302  may provide recommendations as to which smells qualify as odors or fragrances for an owner of the mobile communication device  110 . The personal smell recommender  302  may access data in a personal smell cache  304  local to the mobile communication device  110  and/or a personal smell database  306  local or remote from the mobile communication device  110 . The personal smell cache  304  and/or the personal smell database  306  may store smells encountered by the user of the mobile communication device  110  as well as the user&#39;s preferences with regard to those smells. In one embodiment, a user may only require his or her preferences for a smell once and that preference is then stored in the personal smell cache  304  and/or the personal smell database  306 . 
     A spoken dialog system  308  allows a user to speak to and receive verbal instructions or queries from the mobile communication device  110 . The spoken dialog system  308  may access a personal language database  310  that is specific to a user so that smells can be described in terms that the user understands. For example, the spoken dialog system  308 , upon the mobile communication device  110  detecting a new smell (e.g., based on information provided by an odor mitigation system  102  of a vehicle), may ask the user to describe the smell and to indicate whether they like or dislike the smell, as well as how severe or strong the smell is. The spoken dialog system  308  may then update the personal language database  310  to include terms that correspond to the new smell as well as log the user&#39;s preferences in the personal smell cache  304  and the personal smell database  306 . If the user doesn&#39;t have any specific preferred terms for a smell, the spoken dialog system  308  may use default terms or terms obtained from other users&#39; devices to get the conversation started and update the terms as the user provides a description. In one embodiment, the personal language database  310  may be included within a shared database that includes the information for personal language database  310 , personal smell cache  304 , and personal smell cache  306 . The spoken dialog system  308  and a speech and syntheses and display system  312  may interact with the user using a speech recognition and a human machine interface (HMI)  314 . For example, the speech recognition and HMI  314  may use a microphone  316 , touch screen  318 , speaker  320 , or any other input or output devices to interact with or receive input from a user. In one embodiment, the speech and syntheses and display system  312 , HMI  314 , microphone  316 , screen  318 , and speaker  320  may be part build into a vehicle, such as part of a vehicle infotainment system or in-dash computing system which can be accessed using a user&#39;s mobile device. For example, a user may link or communicate with the in-dash computing system or vehicle infotainment system using Ford® SmartDeviceLink™ or other system or software. 
     The mobile communication device  110  may provide personal smell preferences to the odor mitigation system  102  of a vehicle in which a user is currently an occupant. The mobile communication device  110  may receive descriptions or indications of a current smell (e.g., in human language or in chemical or physical property descriptions of the air) in the vehicle from the odor mitigation system  102  and respond with the user&#39;s preferences or response to the smell. In one embodiment, if a new smell is encountered the mobile communication device  110  may prompt the user for their input or response, while if it is a known smell (e.g., has an entry in the personal smell cache or personal smell database) the mobile communication device  110  may simply provide the user&#39;s preferences without bothering or querying the occupant. 
     The mobile communication device  110  may communicate with mobile communication devices  322 ,  324  in the same or different vehicles to obtain information about encountered smells, human descriptions of smells, or the like. For example, as a user travels between vehicles, the mobile communication device  110  may obtain information about smells that others have encountered so that the mobile communication device  110  may provide a guess as to the user&#39;s preferences or language which the user will understand. Having this information may speed up the process for the user in understanding a query about a smell and/or providing the user&#39;s own specific preferences with respect to that smell. 
       FIG. 4  is a schematic block diagram illustrating some components of an odor mitigation system  102 , according to one embodiment. The odor mitigation system  102  includes one or more processors  402 , an odor detection component  404 , an agreeableness component  406 , and a mitigation component  408 . The components  402 - 408  are given by way of illustration only and may not all be included in all embodiments. In fact, some embodiments may include only one or any combination of two or more of the components  402 - 408 . For example, some of the components  402 - 408  may be located outside or separate from the odor mitigation system  102 . Furthermore, the components  402 - 408  may include hardware, processors, computer readable instructions, or a combination of both to perform the functionality and provide the structures discussed herein. 
     The processors  402  may include any type of processors or processing circuits. In one embodiment, the processors  402  may include a conventional desktop, laptop, or server central processing unit (CPU). In one embodiment, the processors  402  may include multiple parallel processors such as those found in a graphics processing unit (GPU), accelerated processing unit (APU), or neural processing unit (NPU). Parallel processing may be helpful for performing the computations required by a neural network. 
     The odor detection component  404  is configured to detect a known smell based on the one or more odor sensors. For example, the odor detection component  404  may receive sensor outputs or sensor signatures provided by odor sensors. The odor detection component  404  may detect the known smell by matching signals or parameters from the one or more odor sensors with attributes of a smell logged in a smell database or smell cache. The odor detection component  404  may also match information from the one or more odor sensors with a description provided by a human. The odor detection component  404  may match sensor data to a known odor based on chemical signatures, physical air properties, or any other sensor data. 
     The agreeableness component  406  is configured to determine whether the known smell is agreeable to one or more passengers of the vehicle. For example, the agreeableness component  406  communicates a chemical, human language, or other description to the one or more passengers or their personal devices. The personal devices or passengers may then respond to indicate how the passengers perceive the smell. The agreeableness component  406  may determine whether the known smell is agreeable to the one or more passengers based on an indication received from a mobile device for each of the one or more passengers. For example, each passenger or device may provide a “vote” for whether they find the smell agreeable (fragrance) or disagreeable (odor) as well as how severe the odor is. An indication may include a preference of a specific passenger stored on a mobile communication device or a response by the passenger to a query about the known smell. 
     The mitigation component  408  is configured to control one or more odor control devices to mitigate the known smell if the known smell is not agreeable to the one or more passengers of the vehicle. For example, the mitigation component  408  may send signals to any mitigation device such as an HVAC system, window, chemical source, fragrance source, or the like, to mask, remove, or otherwise reduce an odor. The mitigation component  408  may identify a mitigation procedure for mitigating a known or unknown smell or odor. For example, the mitigation component  408  may identify the mitigation procedure by identifying a known smell in a mitigation database, the mitigation database indicating a mitigation procedure for the known smell. As another example, if it is an unknown smell or there is no known mitigation procedure, the mitigation component  408  may query a remote database or other vehicles for mitigation procedures. In one embodiment, a default mitigation procedure may be used in cases where no specific mitigation procedure for the smell can be obtained from local or remote sources. One of a plurality of default mitigation procedures may be selected and tried. By tracking how the smell reacts, based on sensor data and user perceptions, the mitigation component  408  may learn how best to handle the new smell and share that with others. In one embodiment, the mitigation component  408  uses a mitigation database (which may be part of a smell cache or smell database) that includes a database specific to the vehicle, wherein the database specific to the vehicle indicates adjustments to mitigation procedures based on specific attributes of the vehicle. The mitigation database may include a shared database shared by a plurality of vehicles, wherein the shared database matches one or more mitigation procedures from another vehicle with the known smell. 
       FIG. 5  a schematic flow chart diagram illustrating a method  500  for mitigating an odor or smell, according to one embodiment. For example, the method  500  may be performed by an odor mitigation system  102 , such as the odor mitigation systems of  FIG. 1, 2 , or  4 . The method begins and an odor detection component  404  detects  502  a known smell using on one or more odor sensors in a vehicle. An agreeableness component  406  determines  504  whether the known smell is agreeable to one or more passengers of the vehicle. For example, each device or passenger may provide a “vote” indicating whether the odor is agreeable or disagreeable as well as how severe or strong the odor is. The agreeableness component  406  may determine  504  whether to treat the smell as an odor or fragrance based on these responses. A mitigation component  408  mitigates  506  the known smell using one or more odor control devices if the known smell is not agreeable to the one or more passengers of the vehicle. 
       FIG. 6  a schematic flow chart diagram illustrating a method  600  for processing sensor data and mitigating odors based on passenger preferences, according to one embodiment. For example, the method  600  may be performed by an odor mitigation system  102  and/or a mobile communication device  110 . The odor mitigation system  102  collects  602  or samples raw measurements from all the sensors in the vehicle and place the measurements (e.g., sample distributions) into a vector with labels. The measurements may indicate a value for a measured parameter such as temperature, air pressure, particle count, humidity, the presence and amount of a chemical or compound, or any other measurement values. The odor mitigation system  102  scales and offsets  604  vector elements into useful units. For example, the measurements may be converted to a desired format or type (such as a random variable) or may be scaled by a multiplier. For example, the measurements may be offset by an augend and then marshalled into a vector of chemical activity values. The odor mitigation system  102  reduces  606  the dimensionality of the vector by removing or combining duplicative measurements (e.g., distributions using feature extraction) into a single measurement within the vector. The odor mitigation system  102  may forward the resulting vector to any occupant mobile communication device  110  to get their preferences. 
     Each mobile communication device  110  may check  608  to see if the resulting vector has a matching smell or odor in a personal cache or database. If there is no matching odor or smell (No at  608 ), the mobile communication device  110  may query  610  the passenger or user (e.g., occupant) of the device for their preference on the smell. Based on the response, the mobile communication device  110  determines  612  if the smell is an odor or fragrance as well as the severity or strength of the smell. If there is a matching odor or smell (Yes at  608 ), the mobile communication device  110  may determine  612  if the smell is an odor or fragrance as well as the severity or strength of the smell based on data stored in a personal smell database or personal smell cache. 
     Each mobile communication device  110  may provide a corresponding occupants&#39; preferences and the odor mitigation system  102  determines  614  whether the smell represented by the vector is an odor or fragrance. If the smell is a fragrance (Fragrance at  614 ), the odor mitigation system  102  ignores it and returns to collecting  602  sensor measurements. If the smell is an odor (Odor at  614 ), the odor mitigation system  102  determines  616  whether there is a known mitigation strategy for the odor in the current vehicle. If there is a known mitigation strategy (Yes at  616 ), the odor mitigation system  102  implements  620  the mitigation strategy to remove the odor. If there is not a known mitigation strategy (No at  616 ), the odor mitigation system  102  learns  618  a new mitigation strategy specific to the odor while eliminating the odor. For example, the odor mitigation system  102  may implement a default mitigation strategy or obtain a strategy from another vehicle or online database. The odor mitigation system  102  may learn  618  by implementing  620  the strategy and tracking how well the odor is removed based on sensor measurements and/or occupant&#39;s perception of a reduction, increase, or no change in the smell. Upon mitigation of the smell (e.g., after implementing  620  the mitigation strategy) the odor mitigation system  102  may return to collecting  602  sensor measurements. 
       FIG. 7  is a schematic flow chart diagram illustrating a method  700  for learning a mitigation strategy during attempting mitigation of an odor or smell, according to one embodiment. The method  700  may be performed, for example, by an odor mitigation system  102 . 
     The odor mitigation system  102  may get  702  sensor data and look up a mitigation strategy, if any, in the smell cache (e.g., a personal, vehicle specific, or remote cache or database). The odor mitigation system  102  determines  704  what parts of the mitigation strategy can be implemented on a current vehicle. The odor mitigation system  102  collects  706  raw measurements from all the sensors and places them into a vector with labels, scales and offsets  708  the vector elements into useful units, and reduces  710  dimensionality of the vector. The odor mitigation system  102  determines  712  a rate of improvement of the smell. The rate of improvement may be determined  712  by additional sensor measurements, querying the user or user device after mitigation has begun, or the like. The odor mitigation system  102  modifies  714  the mitigation strategy to maximize the rate of odor mitigation while preserving fragrances. For example, the odor mitigation system  102  may identify mitigation steps or portions of a mitigation procedure that led to the fastest odor mitigation or may periodically introduce a random mitigation procedure and measure how the rate of improvement changes. The odor mitigation system  102  determines  716  whether the odor is sufficiently mitigated  716 , such as by taking additional sensor measurements or querying an occupant or device. If the odor is sufficiently mitigated (Yes at  716 ), the odor mitigation system  102  stores  718  the (improved) mitigation strategy for later recall when this odor is encountered. If the odor is not sufficiently mitigated (No at  716 ), the odor mitigation system  102  may begin again to collect  706  sensor data and further modify or track the mitigation. 
       FIG. 8  is a schematic flow chart diagram illustrating a method  800  for determining a user&#39;s preferences with regard to a smell, according to one embodiment. The method  800  may be performed, for example, by a mobile communication device  110  of an occupant of a vehicle. 
     The mobile communication device  110  receives  802  a request to evaluate a smell in the vehicle. For example, the mobile communication device  110  may receive  802  the request from an odor mitigation system  102  of a vehicle. The request may include a description of the smell of interest, such as a vector including sensor measurements and/or human language labels. The mobile communication device  110  determines  804  whether there is a match for the smell in a personal smell cache corresponding to the mobile communication device  110  or user of the mobile communication device  110 . For example, the mobile communication device  110  may search a database for an entry that corresponds to a description received in the request. If there is a match (Yes at  804 ), the mobile communication device  110  gets  806  the smell&#39;s classification and intensity from a smell cache for the user (e.g., stored locally or remotely from the mobile communication device  110 ) and then responds  808  to the request with the classification (e.g., odor or fragrance) and intensity (e.g., on a scale from 1-10). If there is not a match (No at  804 ), the mobile communication device  110  uses a spoken dialog system to survey  810  an occupant using the mobile communication device  110  to determine whether a smell is detected by the occupant, whether the smell is an odor or fragrance for that occupant, and/or how the occupant would rate the intensity of the smell. The user&#39;s responses, including any terms the user used during the dialog, may be put  812  in a smell cache for later retrieval and responds  808  to the request with the classification and intensity. 
       FIG. 9  is a schematic flow chart diagram illustrating a method  900  for learning a new mitigation strategy while eliminating an odor and retaining the fragrances, according to one embodiment. The method  900  may be performed by an odor mitigation system  102 , for example. 
     The odor mitigation system  102  receives  902  a request to remove an unknown odor. For example, an occupant&#39;s device may send a request to remove an odor with a description of the odor. The odor may include a human language description or a sensor description pulled from a smell cache on the occupant&#39;s mobile communication device  110 . As another example, a user may speak or interact directly with the odor mitigation system  102  or an in-dash computing system to provide a human language description and request that the odor be removed. The odor mitigation system  102  gets  904  the smell vector for the smell (e.g., see  602 ,  604 , and  606  of FIG.  6 ). The odor mitigation system  102  may also request  906  information from the occupant or mobile computing device  110  about the nature and intensity of the odor (see e.g.,  FIGS. 6 and 7 ). For example, it may request a description of the odor and the occupant&#39;s perception of the odor. 
     The odor mitigation system  102  performs  908  mitigation to reduce or remove the odor (see e.g.,  FIGS. 6 and 7 ). The odor mitigation system  102  saves  910  the old smell vector (e.g., from  904 ) and get a new smell vector. The odor mitigation system  102  again requests  912  information from the mobile device about the nature and intensity of the odor. Based on the updated vector and user&#39;s response to the request  912 , the odor mitigation system  102  determines  914  whether the odor is sufficiently mitigated. If the odor is not sufficiently mitigated (No at  914 ), the odor mitigation system  102  uses chemistry rules or other rules to change  916  the mitigation strategy based on changes in the smell vector and the resulting psychological effect on the occupant and proceeds to perform  908  mitigation. If the odor is sufficiently mitigated (Yes at  914 ), the odor mitigation system  102  estimates  918  the smell vector for the odor (but may omit vectors for the fragrances) and stores  920  the odor vector and the mitigation strategy in a smell cache. 
       FIG. 10  is a schematic flow chart diagram illustrating a method  1000  for interaction between an odor mitigation system and a mobile communication device of an occupant, according to one embodiment. The method  100  may be performed by an odor mitigation system  102  and a mobile communication device  110 , for example. For example, the mobile communication device may determine individually if the smell is an odor or fragrance to a user. 
     An odor mitigation system  102  may wait  1002  for a complaint about a smell in the vehicle. The complaint may come from a mobile communication device  110  or directly from a user via an audio system or speaker of the vehicle. The odor mitigation system  102  may use a spoken dialog system (of the vehicle or a mobile communication device  110 ) to interacts  1004  with the occupant to determine the nature of the smell. For example, the odor mitigation system  102  may get terms or a description from the user describing the smell, whether it is an odor or fragrance, and/or how strong it is. The odor mitigation system  102  places  1006  the descriptive information about the smell in the publish/subscribe system (e.g., a publish/subscribe database stored by the odor mitigation system  102 ). The odor mitigation system  102  waits for the occurrence of an odor mitigation event  1008 , such as the performance of part or all of a mitigation procedure. The odor mitigation system  102  follows up  1010  with the user about the success of mitigation  1010 . For example, the odor mitigation system  102  may receive a verbal response or response from a mobile communication device  110  indicating whether the user perceives an improvement, decay, or maintenance in the strength of the odor. If the odor is not sufficiently mitigated (No at  1012 ) the odor mitigation system  102  may return to placing  1006  descriptive information in the publish/subscribe system and waiting  1008  for a mitigation event. If the odor is sufficiently mitigated (Yes at  1012 ), the odor mitigation system  102  may wait for additional complaints about smells, if any. 
     An occupant&#39;s mobile communication device  110  may access information within the publish/subscribe database to respond to queries or vote on the need for mitigation. A mobile communication device  110  waits  1014  for a request about a user reported smell and checks  1016  the publish/subscribe system for a user reported smell. If there is/are user reported smells, the mobile communication device  110  responds  1018  with the current user report for smell including a user&#39;s description of the smell as a fragrance or odor. The mobile communication device  110  may wait  1014  for and respond  1018  to additional requests as needed. 
     Referring now to  FIG. 11 , a block diagram of an example computing device  1100  is illustrated. Computing device  1100  may be used to perform various procedures, such as those discussed herein. In one embodiment, the computing device  1100  can function as an odor mitigation system  102 , mobile communication device  110 , or the like. Computing device  1100  can perform various monitoring functions as discussed herein, and can execute one or more application programs, such as the application programs or functionality described herein. Computing device  1100  can be any of a wide variety of computing devices, such as a desktop computer, in-dash computer, vehicle control system, a notebook computer, a server computer, a handheld computer, tablet computer and the like. 
     Computing device  1100  includes one or more processor(s)  1102 , one or more memory device(s)  1104 , one or more interface(s)  1106 , one or more mass storage device(s)  1108 , one or more Input/Output (I/O) device(s)  1110 , and a display device  1130  all of which are coupled to a bus  1112 . Processor(s)  1102  include one or more processors or controllers that execute instructions stored in memory device(s)  1104  and/or mass storage device(s)  1108 . Processor(s)  1102  may also include various types of computer-readable media, such as cache memory. 
     Memory device(s)  1104  include various computer-readable media, such as volatile memory (e.g., random access memory (RAM)  1114 ) and/or nonvolatile memory (e.g., read-only memory (ROM)  1116 ). Memory device(s)  1104  may also include rewritable ROM, such as Flash memory. 
     Mass storage device(s)  1108  include various computer readable media, such as magnetic tapes, magnetic disks, optical disks, solid-state memory (e.g., Flash memory), and so forth. As shown in  FIG. 11 , a particular mass storage device is a hard disk drive  1124 . Various drives may also be included in mass storage device(s)  1108  to enable reading from and/or writing to the various computer readable media. Mass storage device(s)  1108  include removable media  1126  and/or non-removable media. 
     I/O device(s)  1110  include various devices that allow data and/or other information to be input to or retrieved from computing device  1100 . Example I/O device(s)  1110  include cursor control devices, keyboards, keypads, microphones, monitors or other display devices, speakers, printers, network interface cards, modems, and the like. 
     Display device  1130  includes any type of device capable of displaying information to one or more users of computing device  1100 . Examples of display device  1130  include a monitor, display terminal, video projection device, and the like. 
     Interface(s)  1106  include various interfaces that allow computing device  1100  to interact with other systems, devices, or computing environments. Example interface(s)  1106  may include any number of different network interfaces  1120 , such as interfaces to local area networks (LANs), wide area networks (WANs), wireless networks, and the Internet. Other interface(s) include user interface  1118  and peripheral device interface  1122 . The interface(s)  1106  may also include one or more user interface elements  1118 . The interface(s)  1106  may also include one or more peripheral interfaces such as interfaces for printers, pointing devices (mice, track pad, or any suitable user interface now known to those of ordinary skill in the field, or later discovered), keyboards, and the like. 
     Bus  1112  allows processor(s)  1102 , memory device(s)  1104 , interface(s)  1106 , mass storage device(s)  1108 , and I/O device(s)  1110  to communicate with one another, as well as other devices or components coupled to bus  1112 . Bus  1112  represents one or more of several types of bus structures, such as a system bus, PCI bus, IEEE bus, USB bus, and so forth. 
     For purposes of illustration, programs and other executable program components are shown herein as discrete blocks, although it is understood that such programs and components may reside at various times in different storage components of computing device  1100 , and are executed by processor(s)  1102 . Alternatively, the systems and procedures described herein can be implemented in hardware, or a combination of hardware, software, and/or firmware. For example, one or more application specific integrated circuits (ASICs) or a system on a chip (SoC) can be programmed to carry out one or more of the systems and procedures described herein. 
     EXAMPLES 
     The following examples pertain to further embodiments. 
     Example 1 is a method for mitigating odors that includes detecting a known smell using on one or more odor sensors in a vehicle. The method includes determining whether the known smell is agreeable to one or more passengers of the vehicle. The method includes mitigating the known smell using one or more odor control devices if the known smell is not agreeable to the one or more passengers of the vehicle. 
     In Example 2, the determining whether the known smell is agreeable to the one or more passengers of Example 1 includes receiving an indication from a mobile device for each of the one or more passengers. 
     In Example 3, the receiving the indication of Example 2 includes one or more of a preference of a specific passenger stored on the mobile device or a response by the passenger to a query about the known smell. 
     In Example 4, the method of any of Examples 1-3 further includes identifying a mitigation procedure for mitigating the known smell, wherein identifying the mitigation procedure includes identifying the known smell in a mitigation database, the mitigation database indicating a mitigation procedure for the known smell. 
     In Example 5, the mitigation database of Example 4 includes one or more of: a database specific to the vehicle, wherein the database specific to the vehicle indicates adjustments to mitigation procedures based on specific attributes of the vehicle; and a shared database shared by a plurality of vehicles, wherein the shared database matches one or more mitigation procedures from another vehicle with the known smell. 
     In Example 6, the detecting the known smell in any of Examples 1-5 includes matching signals or parameters detected by the one or more odor sensors with attributes of a smell logged in a smell database. 
     In Example 7, the detecting the known smell in any of Examples 1-6 includes matching information from the one or more odor sensors with a description provided by a human. 
     In Example 8, the one or more odor sensors in any of Examples 1-7 includes at least one electronic nose and wherein the one or more odor control devices include one or more of a window controller, an HVAC circulation controller, an air filter, a fragrance source, and a chemical source including a chemical for reacting with a cause of the known smell. 
     Example 9 is computer readable storage media storing instructions that, when executed by one or more processors, cause the one or more processors to implement a method or realize a system or apparatus as in any of Examples 1-8. 
     Example 10 is a system or device that includes means for implementing a method or realizing a system or apparatus as in any of Examples 1-9. 
     In the above disclosure, reference has been made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific implementations in which the disclosure may be practiced. It is understood that other implementations may be utilized and structural changes may be made without departing from the scope of the present disclosure. References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Implementations of the systems, devices, and methods disclosed herein may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed herein. Implementations within the scope of the present disclosure may also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are computer storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, implementations of the disclosure can comprise at least two distinctly different kinds of computer-readable media: computer storage media (devices) and transmission media. 
     Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium, which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. 
     An implementation of the devices, systems, and methods disclosed herein may communicate over a computer network. A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links, which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, an in-dash vehicle computer, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, various storage devices, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices. 
     Further, where appropriate, functions described herein can be performed in one or more of: hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. Certain terms are used throughout the description and claims to refer to particular system components. The terms “modules” and “components” are used in the names of certain components to reflect their implementation independence in software, hardware, circuitry, sensors, or the like. As one skilled in the art will appreciate, components may be referred to by different names. This document does not intend to distinguish between components that differ in name, but not function. 
     It should be noted that the sensor embodiments discussed above may comprise computer hardware, software, firmware, or any combination thereof to perform at least a portion of their functions. For example, a sensor may include computer code configured to be executed in one or more processors, and may include hardware logic/electrical circuitry controlled by the computer code. These example devices are provided herein purposes of illustration, and are not intended to be limiting. Embodiments of the present disclosure may be implemented in further types of devices, as would be known to persons skilled in the relevant art(s). 
     At least some embodiments of the disclosure have been directed to computer program products comprising such logic (e.g., in the form of software) stored on any computer useable medium. Such software, when executed in one or more data processing devices, causes a device to operate as described herein. 
     While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art that various changes in form and detail can be made therein without departing from the spirit and scope of the disclosure. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents. The foregoing description has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. Further, it should be noted that any or all of the aforementioned alternate implementations may be used in any combination desired to form additional hybrid implementations of the disclosure. 
     Further, although specific implementations of the disclosure have been described and illustrated, the disclosure is not to be limited to the specific forms or arrangements of parts so described and illustrated. The scope of the disclosure is to be defined by the claims appended hereto, any future claims submitted here and in different applications, and their equivalents.