Patent Application
20250061493
The present disclosure generally relates to real-estate property evaluations, and more particularly, to an AI-based automated system and method for real-time property evaluation based on a user request and conversation-related data.
Traditional methods of property valuation and condition assessment often rely on subjective judgments and manual processes, which can lead to inaccuracies and inefficiencies. The process may involve home inspections, appraisals, and other time-consuming procedures.
Some existing solutions may involve a basic automation that allows the real-estate practitioners to evaluate a property based on large amounts of data including comparisons, etc. For example, U.S. Ser. No. 10/192,275 discloses a system for automated real estate valuation. The system uses an algorithm, where common features are removed and remaining features are given a tokenized grouping and weight via an algorithm to explain price per square foot differences between sold properties. These weights are used to determine a calculated value of the specific property by determining a calculated value per square foot based upon the weight of the features associated with the specific property.
U.S. Ser. No. 10/896,449 discloses automatically determining current value of real-estate. The system collects user inputs, applies some adjustments and produces property valuation based on the adjustments. U.S. Ser. No. 11/093,992 discloses smart matching of user to the property. Systems and methods are described for efficiently meeting the needs of property buyers, sellers, and agents. Attributes of the user and of each property are combined and processed by a machine learning model. The output represents a prediction that a user will choose to view the property. As another example, U.S. Ser. No. 11/595,333B2 discloses interaction-driven chatbot system used for real-estate context. Artificial intelligence systems utilizing a chat driven interface that includes a chat portion and a display portion are disclosed. The user may interact with the chat portion of the interface in a human understandable and comfortable manner (e.g., through natural language). The artificial intelligence system uses the context of the interactions in the chat portion of the interface to drive the presentation of content in the display portion.
However, these solutions lack the important features of accurate comprehensive evaluation of the property using, for example, audio, video and imaging data to generate accurate predictive evaluation parameters that may be processed to generate property evaluation reports including repair recommendations, estimates, rankings, etc. Additionally, these applications do not mention the use of fine-tuned models based on pre-trained language models used to processing comprehensive multi-formatted property-related data, which can offer a significant improvement in accuracy of property evaluation and its efficiency compared to traditional property evaluation techniques and methods.
Accordingly, a system and method for AI-based automated real-time property evaluation based on a user request and conversation-related data are desired.
This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter's scope.
One embodiment of the present disclosure provides a system for an automated property evaluation based on property-related data, including a processor of a property evaluation server (PES) node configured to host a machine learning (ML) module coupled to a summarizer module and connected to at least one user-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a user request comprising target property data from the at least one user-entity node; parse the user request to extract a plurality of key classifying features; activate a chatbot running on the PES node to acquire conversation data from the user; query a local database to retrieve local historical properties'-related data based on the plurality of key classifying features and the conversation data; generate at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties'-related data; and provide the at least one classifier vector to the ML module configured to generate a predictive model for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report.
Another embodiment of the present disclosure provides a method that includes one or more of: acquiring a user request comprising target property data from the at least one user-entity node; parsing the user request to extract a plurality of key classifying features; activate a chatbot running on the PES node to acquire conversation data from the user; querying a local database to retrieve local historical properties'-related data based on the plurality of key classifying features and the conversation data; generating at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties'-related data; and providing the at least one classifier vector to the ML module configured to generate a predictive model for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report.
Another embodiment of the present disclosure provides a computer-readable medium including instructions for acquiring a user request comprising target property data from the at least one user-entity node; parsing the user request to extract a plurality of key classifying features; activate a chatbot running on the PES node to acquire conversation data from the user; querying a local database to retrieve local historical properties'-related data based on the plurality of key classifying features and the conversation data; generating at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties'-related data; and providing the at least one classifier vector to the ML module configured to generate a predictive model for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report.
Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings may contain representations of various trademarks and copyrights owned by the Applicant. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicant. The Applicant retains and reserves all rights in its trademarks and copyrights included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings:
As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.
Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, itis to be understood that this disclosure is illustrative and exemplary of the present disclosure and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.
Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.
Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such a term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term-differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.
Regarding applicability of 35 U.S.C. § 112, ¶6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase “means for” or “step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.
Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”
The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subject matter disclosed under the header.
The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of the predictive generation of the property evaluation parameters, embodiments of the present disclosure are not limited to use only in this context.
The present disclosure provides a system, method and computer-readable medium for an AI-based automated for real-time property evaluation based on predictive analytics of property-related data. In one embodiment, the system overcomes the limitations of existing methods of property assessment and/or evaluation by employing fine-tuned models derived from pre-trained language models to extract and process the user request and conversation information, irrespective of data format, style, or data type. By leveraging the capabilities of the pre-trained language models and predictive models, the disclosed approach offers a significant improvement over existing solutions discussed above in the background section.
In one embodiment of the present disclosure, the system provides for an AI and machine learning (ML)—generated property evaluation parameters for a summarizer module configured to generate a property evaluation report. In one embodiment, an automated conversation model may be generated to provide for recommendation parameters associated with the real-estate agent and the target properties associate with user-entities. The predictive model may use historical properties'-related data collected at the current real-estate office location(s) and at other real-estate offices of the same type located within a certain range from the current location or even located globally. The relevant target properties' data may include data related to other properties having the same parameters such as type of location, size, physical conditions, appraisals and sales data, language of the jurisdiction, nationality of the owners or locations, etc. In one embodiment, the relevant properties' data may indicate successfully closed sales based on analytics, conditions, times of the year, etc. This way, the best matching agent(s) may be directed to respond to a real-life user request based on current user entity-related data and historical data of users associated with the properties having the same characteristics.
In one embodiment, to enhance this process, the system may integrate advanced technologies discussed above, such as Artificial Intelligence (AI) and machine-learning (ML) and Blockchain. The AI may be leveraged for several key functions discussed below. In one embodiment, the AI-based property evaluation system may be used to predict the condition of a home, the costs associated with upgrading the home, and the resulting value. The system may employ a summarizer module for generation of a property evaluation report including various evaluation metrics.
In one embodiment, the data sets used for property evaluation may include, but not limited, to Image/Video analysis of the condition of the property, Public Data (Sqdt, bedrooms, bath, year built, etc.), Sold Data, Inspection reports and disclosures reports, Consumer Survey of condition of their home/repairs made, other home scores and estimates based on property condition and Estimated Repair Costs for the geographic Area based on labor and materials. The predictive report may produce accurate true cost of home ownership with preventative maintenance suggestions.
The predictive models of the disclosed embodiments provide for more accurate valuation of the property. The Home Scan condition score may compare the property to other homes in the neighborhood. The predictive outputs may include repair plan and cost estimation. A search based off of the repair cost and after repair value may be provided within the property evaluation report.
In one embodiment, a summarizer module may be implemented to generate estimates and key condition functionality of the home based on the predictive property evaluation parameters based on reports and estimated repair costs. In this interactive search, data from PDFs, images, and public data may be used to create an idea of the property condition. A summary may be provided with the estimated cost of repairs and grading on the severity of the problem.
In one embodiment, users will be able to ask a LLM chatbot about the property. The chatbot may be couple to the AI module which is trained on data from these reports as well as other data to provide a deeper understanding and further training of the models indicating which data points are key to include when summarizing the property. Furthermore, data may be sourced showing where the interactive search found the problem or clearance for the specific property.
Additionally, the disclosed comprehensive property evaluation system may incorporate blockchain technology to ensure the transparency and immutability of transactions, providing a secure and trustworthy platform. By embedding these advanced technologies, the disclosed automated property evaluation system, advantageously, offers a sophisticated and secure solution.
As discussed above, in one disclosed embodiment, the AI/ML technology may be combined with a blockchain technology for secure use of the user entity-related data and the target property data. The property evaluation report reviewers (e.g., broker entities) can accept or edit the machine generated property evaluation report, and send back to the requesting user. In one embodiment, a blockchain consensus may need to be implemented prior to provision of the final property evaluation report to the requesting user. In one embodiment, the property evaluation report and related documents may be stored in a form of uniquely minted NFTs on the blockchain ledger.
Referring to
The user request data may have language indicator metadata representing the language of the user 111. The conversation data may refer to any communications via a chatbot 114 application. In one embodiment, the conversation data may be processed by the PES node 102 using the pre-trained large language models. The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata. In other words, the key features of the conversation data may be, advantageously, derived from the conversation data based on the language of the communication, such as call or email, text or other communication.
In one embodiment, the language indicator may serve as a kind of a linguistic profile associated with the user request. The language indicator may guide the AI/ML module 107 in dynamically tailoring the conversation recommendation parameters for the chatbot 114 processing. Depending on the language indicated, the PES node 102 could engage specialized language models or apply unique natural language processing techniques optimized for that language.
Regarding the global reach of the disclosed system and method, a cultural intelligence layer may be added to the language indicator. The goal of this layer is for the system to not only recognize the language, but also adapt its recommendations and interactions to be culturally sensitive and appropriate for the user 111. In one embodiment, the disclosed system may employ integrated translation capabilities. This may allow both the user 111 and the real-estate agent associated with the PES 102 to communicate effortlessly via the chatbot 114, no matter where they are in the world or what languages they use. The language indicator metadata may support and/or trigger this feature, making the system truly globally effective.
The PES node 102 may query a local historical properties'-related database 103 for the historical local properties'-related data based on the user request data associated with the current user entity 101 node and the conversation data. The PES node 102 may acquire relevant remote properties'-related data from a remote database 106 residing on the cloud server 105. The properties'-related data in the database 106 may be collected from other real-estate facilities of the same type. The remote properties'-related data may be collected from the user entities of the same (or similar) type, age, gender, language, location, etc. as the local user entity 101 based in part on data extracted from the user profile data and the conversation data.
The PES node 102 may generate a feature vector or classifier based on the user entity-related data, a request data and the collected heuristics data (i.e., pre-stored local historical data 103 and remote historical data 106). The PES node 102 may ingest the feature vector/classifier data into an AI/ML module 107. The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector/classifier data to predict property evaluation parameters for the summarizer module for automatically generating property evaluation report that may be provided to the broker-entity nodes 113. The property evaluation parameters may be further analyzed by the PES node 102 prior to generation of the actual property evaluation report. In one embodiment, the property evaluation report may be used for adjustment of the conditions and financing terms of a property sale.
Referring to
The user request data may have language indicator metadata representing the language of the user 111. The conversation data may refer to any communications via a chatbot 114 application. In one embodiment, the conversation data may be processed by the PES node 102 using the pre-trained large language models. The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata. In other words, the key features of the conversation data may be, advantageously, derived from the conversation data based on the language of the communication, such as call or email, text or other communication.
The PES node 102 may query a local historical properties'-related database 103 for the historical local properties'-related data based on the user request data associated with the current user entity 101 node and the conversation data. The PES node 102 may acquire relevant remote properties'-related data from a remote database 106 residing on the cloud server 105. The properties'-related data in the database 106 may be collected from other real-estate facilities of the same type. The remote properties'-related data may be collected from the user entities of the same (or similar) type, age, gender, language, location, etc. as the local user entity 101 based in part on data extracted from the user profile data and the conversation data.
The PES node 102 may generate a feature vector or classifier based on the user entity-related data, a request data and the collected heuristics data (i.e., pre-stored local historical data 103 and remote historical data 106). The PES node 102 may ingest the feature vector/classifier data into an AI/ML module 107. The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector/classifier data to predict property evaluation parameters for the summarizer module for automatically generating property evaluation report that may be provided to the broker-entity nodes 113. The property evaluation parameters may be further analyzed by the PES node 102 prior to generation of the actual property evaluation report. In one embodiment, the property evaluation report may be used for adjustment of the conditions and financing terms of a property sale.
In one embodiment, the PES node 102 may receive the property evaluation parameters from a permissioned blockchain 110 ledger 109 based on a consensus from the broker-entity nodes 113 confirming the questions and comments to be presented by the chatbot 114 the user 111 of the user entity 101. Additionally, confidential historical user-related information and previous users-related conversation-related parameters may also be acquired from the permissioned blockchain 110. The newly acquired user request data with corresponding conversation data parameters may be also recorded on the ledger 109 of the blockchain 110 so it can be used as training data for the predictive model(s) 108.
In this implementation the PES node 102, the cloud server 105, the broker-entity nodes 113 and the user entities(s) 101 may serve as blockchain 110 peer nodes. In one embodiment, local data from the database 103 and remote data from the database 106 may be duplicated on the blockchain ledger 109 for higher security of storage.
The AI/ML module 107 may generate a predictive model(s) 108 to predict the property evaluation parameters for the chatbot 114 in response to the specific relevant pre-stored properties'-related data acquired from the blockchain 110 ledger 109. This way, the current property evaluation parameters may be predicted based not only on the current user 111 associated with the entity 101-related data, but also based on the previously collected heuristics. This way, the most optimal way of handling the user request for the most likely successful closing of the sale or evaluation of the property may be included into the property evaluation report. After the user request data processing and the property evaluation report generation is completed, the related documents may be converted into unique secure NFT assets to be recorded on the blockchain to be used for future predictive models' training.
In one embodiment, as a second round of approval, a blockchain consensus may be achieved among the broker-entities 113 in order to approve the property evaluation report generated by the PES node 102.
Referring to
The PES node 102 is configured to host an AI/ML module 107. As discussed above with respect to
The AI/ML module 107 may generate a predictive model(s) 108 based on the data 202 provided by the PES node 102. As discussed above, the AI/ML module 107 may provide predictive outputs data in the form of property evaluation parameters for automatic generation of property evaluation report (see
In one embodiment, the PES node 102 may continually monitor the conversation data and may detect a parameter that deviates from a previous recorded parameter (or from a median reading value) by a margin that exceeds a threshold value pre-set for this particular parameter. For example, if user's 111 answers change significantly, this may cause a change in evaluation parameters used for generation for the property evaluation report. Accordingly, once the threshold is met or exceeded by at least one parameter associated with the user entity, the PES node 102 may provide the currently acquired user conversation-related parameter to the AI/ML module 107 to generate updated property evaluation parameters for the summarizer module based on the current conversation data.
While this example describes in detail only one PES node 102, multiple such nodes may be connected to the network and to the blockchain 110. It should be understood that the PES node 102 may include additional components and that some of the components described herein may be removed and/or modified without departing from a scope of the PES node 102 disclosed herein. The PES node 102 may be a computing device or a server computer, or the like, and may include a processor 204, which may be a semiconductor-based microprocessor, a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and/or another hardware device. Although a single processor 204 is depicted, it should be understood that the PES node 102 may include multiple processors, multiple cores, or the like, without departing from the scope of the PES node 102 system.
The PES node 102 may also include a non-transitory computer readable medium 212 that may have stored thereon machine-readable instructions executable by the processor 204. Examples of the machine-readable instructions are shown as 214-224 and are further discussed below. Examples of the non-transitory computer readable medium 212 may include an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. For example, the non-transitory computer readable medium 212 may be a Random-Access memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a hard disk, an optical disc, or other type of storage device.
The processor 204 may fetch, decode, and execute the machine-readable instructions 214 to acquire a user request comprising target property data from the at least one user-entity node 101 (see
The processor 204 may fetch, decode, and execute the machine-readable instructions 222 to generate at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties'-related data. Note that the chatbot may represent a real-estate agent in communication with the user-entity node 101 over a secured communication channel generating conversation data. The processor 204 may fetch, decode, and execute the machine-readable instructions 224 to provide the at least one classifier vector to the ML module 107 configured to generate a predictive model 108 for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report.
As a non-limiting example, a consensual approval of the property evaluation report may be associated with a request for additional data such as proof of corrected quotes, sales data, additional sale closing statement, etc. The permissioned blockchain 110 may be configured to use one or more smart contracts that manage transactions for multiple participating nodes and for recording the transactions on the ledger 109.
Referring to
With reference to
Referring to
With reference to
Note that the conversation data may be any of: audio data, video data, imaging data and textual data. At block 316, the processor 204 may extract a language identifier from the user request. At block 318, the processor 204 may derive the plurality of the key classifying features based on the language identifier. At block 320, the processor 204 may retrieve properties'-related data from at least one remote database based on the plurality of the key classifying features and the conversation data, wherein the remote properties'-related data is collected at locations associated with remote real estate outfits of the same type. At block 322, the processor 204 may generate the at least one classifier vector based on the plurality of the key classifying features and the local historical properties'-related data combined with the remote properties'-related data.
At block 324, the processor 204 may continuously monitor the conversation data from the chatbot to determine if at least one value of property-related parameters contained in the conversation data deviates from a previous value of a pre-set corresponding property-related parameter value by a margin exceeding a pre-set threshold value. At block 326, the processor 204 may responsive to the at least one value of the property-related parameters deviating from the pre-set corresponding property-related parameter value by the margin exceeding the pre-set threshold value, generate an updated classifier vector based on the conversation data coming from the chatbot and generate updated property evaluation parameters produced in real-time by the predictive model in response to the updated classifier vector. At block 328, the processor 204 may record the set of set of property evaluation parameters on a permissioned blockchain ledger along with the at least one classifier vector.
At block 330, the processor 204 may retrieve at least one of set of property evaluation parameters for the chatbot from the blockchain responsive to a consensus among user-entity nodes onboarded onto the permissioned blockchain. At block 332, the processor 204 may execute a smart contract to generate at least one NFT corresponding to the property evaluation report comprising a plurality of property evaluation metrics on the permissioned blockchain.
In one disclosed embodiment, the property evaluation parameters' model may be generated by the AI/ML module 107 that may use training data sets to improve accuracy of the prediction of the property evaluation parameters. The property evaluation parameters used in training data sets may be stored in a centralized local database (such as one used for storing local properties' data 103 depicted in
In another embodiment, the AI/ML module 107 may use a decentralized storage such as a blockchain 110 (see
This application utilizes a permissioned (private) blockchain that operates arbitrary, programmable logic, tailored to a decentralized storage scheme and referred to as “smart contracts” or “chaincodes.” In some cases, specialized chaincodes may exist for management functions and parameters which are referred to as system chaincodes. The application can further utilize smart contracts that are trusted distributed applications which leverage tamper-proof properties of the blockchain database and an underlying agreement between nodes, which is referred to as an endorsement or endorsement policy. Blockchain transactions associated with this application can be “endorsed” before being committed to the blockchain while transactions, which are not endorsed, are disregarded. An endorsement policy allows chaincodes to specify endorsers for a transaction in the form of a set of peer nodes that are necessary for endorsement. When a client sends the transaction to the peers specified in the endorsement policy, the transaction is executed to validate the transaction. After a validation, the transactions enter an ordering phase in which a consensus protocol is used to produce an ordered sequence of endorsed transactions grouped into blocks.
In the example depicted in
This can significantly reduce the collection time needed by the host platform 420 when performing predictive model training. For example, using smart contracts, data can be directly and reliably transferred straight from its place of origin (e.g., from the PES node 102 or from databases 103 and 106 depicted in
Furthermore, training of the machine learning model on the collected data may take rounds of refinement and testing by the host platform 420. Each round may be based on additional data or data that was not previously considered to help expand the knowledge of the machine learning model. In 402, the different training and testing steps (and the data associated therewith) may be stored on the blockchain 110 by the host platform 420. Each refinement of the machine learning model (e.g., changes in variables, weights, etc.) may be stored on the blockchain 110. This, advantageously, provides verifiable proof of how the model was trained and what data was used to train the model. Furthermore, when the host platform 420 has achieved a finally trained model, the resulting model itself may be stored on the blockchain 110.
After the model has been trained, it may be deployed to a live environment where it can make recommendation-related predictions/decisions based on the execution of the final trained machine learning model using the predictive parameters. In this example, data fed back from the asset 430 may be input into the machine learning model and may be used to make event predictions such as property evaluation parameters based on the recorded properties'-related data. Determinations made by the execution of the machine learning model (e.g., approval of property evaluation reports, etc.) at the host platform 420 may be stored on the blockchain 110 to provide auditable/verifiable proof. As one non-limiting example, the machine learning model may predict a future change of a part of the asset 430 (the property evaluation parameters—i.e., assessment of the property for the evaluation report). The data behind this decision may be stored by the host platform 420 on the blockchain 110.
In summary, the disclosed embodiments provide for the following novel features.
More Accurate Property Valuation by utilizing predictive models to provide a more accurate valuation of the property based on its current condition and estimated repair costs.
Generation of Home Scan Condition Score for the property by comparing it to other homes in the neighborhood.
Repair Plan and Cost Estimator provides a detailed repair plan and cost estimation based on the predicted condition of the property and geographic labor and material costs.
Search Based on Repair Cost and After Repair Value: allows users to search for properties based on repair costs and the expected value after repairs are made.
Interactive Summarizer offers a comprehensive summary of the property's condition, estimated repair costs, and severity of issues. Users can interact with a chatbot to ask questions about the property's condition and get detailed responses based on the data.
As discussed above, in one embodiment, the features and/or the actions described and/or depicted herein can occur on or with respect to the blockchain 110. The above embodiments of the present disclosure may be implemented in hardware, in computer-readable instructions executed by a processor, in firmware, or in a combination of the above. The computer computer-readable instructions may be embodied on a computer-readable medium, such as a storage medium. For example, the computer computer-readable instructions may reside in random access memory (“RAM”), flash memory, read-only memory (“ROM”), erasable programmable read-only memory (“EPROM”), electrically erasable programmable read-only memory (“EEPROM”), registers, hard disk, a removable disk, a compact disk read-only memory (“CD-ROM”), or any other form of storage medium known in the art.
An exemplary storage medium may be coupled to the processor such that the processor may read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (“ASIC”). In the alternative embodiment, the processor and the storage medium may reside as discrete components. For example,
Embodiments of the present disclosure may comprise a computing device having a central processing unit (CPU) 520, a bus 530, a memory unit 550, a power supply unit (PSU) 550, and one or more Input/Output (I/O) units. The CPU 520 coupled to the memory unit 550 and the plurality of I/O units 560 via the bus 530, all of which are powered by the PSU 550. It should be understood that, in some embodiments, each disclosed unit may actually be a plurality of such units for the purposes of redundancy, high availability, and/or performance. The combination of the presently disclosed units is configured to perform the stages of any method disclosed herein.
Consistent with an embodiment of the disclosure, the aforementioned CPU 520, the bus 530, the memory unit 550, a PSU 550, and the plurality of I/O units 560 may be implemented in a computing device, such as computing device 500. Any suitable combination of hardware, software, or firmware may be used to implement the aforementioned units. For example, the CPU 520, the bus 530, and the memory unit 550 may be implemented with computing device 500 or any of other computing devices 500, in combination with computing device 500. The aforementioned system, device, and components are examples and other systems, devices, and components may comprise the aforementioned CPU 520, the bus 530, the memory unit 550, consistent with embodiments of the disclosure.
At least one computing device 500 may be embodied as any of the computing elements illustrated in all of the attached figures, including the PES node 102 (
With reference to
A system consistent with an embodiment of the disclosure the computing device 500 may include the clock module 510 may be known to a person having ordinary skill in the art as a clock generator, which produces clock signals. Clock signal is a particular type of signal that oscillates between a high and a low state and is used like a metronome to coordinate actions of digital circuits. Most integrated circuits (ICs) of sufficient complexity use a clock signal in order to synchronize different parts of the circuit, cycling at a rate slower than the worst-case internal propagation delays. The preeminent example of the aforementioned integrated circuit is the CPU 520, the central component of modern computers, which relies on a clock. The only exceptions are asynchronous circuits such as asynchronous CPUs. The clock 510 can comprise a plurality of embodiments, such as, but not limited to, single-phase clock which transmits all clock signals on effectively 1 wire, two-phase clock which distributes clock signals on two wires, each with non-overlapping pulses, and four-phase clock which distributes clock signals on 5 wires.
Many computing devices 500 use a “clock multiplier” which multiplies a lower frequency external clock to the appropriate clock rate of the CPU 520. This allows the CPU 520 to operate at a much higher frequency than the rest of the computer, which affords performance gains in situations where the CPU 520 does not need to wait on an external factor (like memory 550 or input/output 560). Some embodiments of the clock 510 may include dynamic frequency change, where the time between clock edges can vary widely from one edge to the next and back again.
A system consistent with an embodiment of the disclosure the computing device 500 may include the CPU unit 520 comprising at least one CPU Core 521. A plurality of CPU cores 521 may comprise identical CPU cores 521, such as, but not limited to, homogeneous multi-core systems. It is also possible for the plurality of CPU cores 521 to comprise different CPU cores 521, such as, but not limited to, heterogeneous multi-core systems, big.LITTLE systems and some AMD accelerated processing units (APU). The CPU unit 520 reads and executes program instructions which may be used across many application domains, for example, but not limited to, general purpose computing, embedded computing, network computing, digital signal processing (DSP), and graphics processing (GPU). The CPU unit 520 may run multiple instructions on separate CPU cores 521 at the same time. The CPU unit 520 may be integrated into at least one of a single integrated circuit die and multiple dies in a single chip package. The single integrated circuit die and multiple dies in a single chip package may contain a plurality of other aspects of the computing device 500, for example, but not limited to, the clock 510, the CPU 520, the bus 530, the memory 550, and I/O 560.
The CPU unit 520 may contain cache 522 such as, but not limited to, a level 1 cache, level 2 cache, level 3 cache or combination thereof. The aforementioned cache 522 may or may not be shared amongst a plurality of CPU cores 521. The cache 522 sharing comprises at least one of message passing and inter-core communication methods may be used for the at least one CPU Core 521 to communicate with the cache 522. The inter-core communication methods may comprise, but not limited to, bus, ring, two-dimensional mesh, and crossbar. The aforementioned CPU unit 520 may employ symmetric multiprocessing (SMP) design.
The plurality of the aforementioned CPU cores 521 may comprise soft microprocessor cores on a single field programmable gate array (FPGA), such as semiconductor intellectual property cores (IP Core). The plurality of CPU cores 521 architecture may be based on at least one of, but not limited to, Complex instruction set computing (CISC), Zero instruction set computing (ZISC), and Reduced instruction set computing (RISC). At least one of the performance-enhancing methods may be employed by the plurality of the CPU cores 521, for example, but not limited to Instruction-level parallelism (ILP) such as, but not limited to, superscalar pipelining, and Thread-level parallelism (TLP).
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ a communication system that transfers data between components inside the aforementioned computing device 500, and/or the plurality of computing devices 500. The aforementioned communication system will be known to a person having ordinary skill in the art as a bus 530. The bus 530 may embody internal and/or external plurality of hardware and software components, for example, but not limited to a wire, optical fiber, communication protocols, and any physical arrangement that provides the same logical function as a parallel electrical bus. The bus 530 may comprise at least one of, but not limited to a parallel bus, wherein the parallel bus carry data words in parallel on multiple wires, and a serial bus, wherein the serial bus carry data in bit-serial form. The bus 530 may embody a plurality of topologies, for example, but not limited to, a multidrop/electrical parallel topology, a daisy chain topology, and a connected by switched hubs, such as USB bus. The bus 530 may comprise a plurality of embodiments, for example, but not limited to:
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ hardware integrated circuits that store information for immediate use in the computing device 500, known to the person having ordinary skill in the art as primary storage or memory 550. The memory 550 operates at high speed, distinguishing it from the non-volatile storage sub-module 561, which may be referred to as secondary or tertiary storage, which provides slow-to-access information but offers higher capacities at lower cost. The contents contained in memory 550, may be transferred to secondary storage via techniques such as, but not limited to, virtual memory and swap. The memory 550 may be associated with addressable semiconductor memory, such as integrated circuits consisting of silicon-based transistors, used for example as primary storage but also other purposes in the computing device 500. The memory 550 may comprise a plurality of embodiments, such as, but not limited to volatile memory, non-volatile memory, and semi-volatile memory. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned memory:
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the communication sub-module 562 as a subset of the I/O 560, which may be referred to by a person having ordinary skill in the art as at least one of, but not limited to, computer network, data network, and network. The network allows computing devices 500 to exchange data using connections, which may be known to a person having ordinary skill in the art as data links, between network nodes. The nodes comprise network computer devices 500 that originate, route, and terminate data. The nodes are identified by network addresses and can include a plurality of hosts consistent with the embodiments of a computing device 500. The aforementioned embodiments include, but not limited to personal computers, phones, servers, drones, and networking devices such as, but not limited to, hubs, switches, routers, modems, and firewalls.
Two nodes can be networked together, when one computing device 500 is able to exchange information with the other computing device 500, whether or not they have a direct connection with each other. The communication sub-module 562 supports a plurality of applications and services, such as, but not limited to World Wide Web (WWW), digital video and audio, shared use of application and storage computing devices 500, printers/scanners/fax machines, email/online chat/instant messaging, remote control, distributed computing, etc. The network may comprise a plurality of transmission mediums, such as, but not limited to conductive wire, fiber optics, and wireless. The network may comprise a plurality of communications protocols to organize network traffic, wherein application-specific communications protocols are layered, may be known to a person having ordinary skill in the art as carried as payload, over other more general communications protocols. The plurality of communications protocols may comprise, but not limited to, IEEE 802, ethernet, Wireless LAN (WLAN/Wi-Fi), Internet Protocol (IP) suite (e.g., TCP/IP, UDP, Internet Protocol version 5 [IPv5], and Internet Protocol version 6 [IPv6]), Synchronous Optical Networking (SONET)/Synchronous Digital Hierarchy (SDH), Asynchronous Transfer Mode (ATM), and cellular standards (e.g., Global System for Mobile Communications [GSM], General Packet Radio Service [GPRS], Code-Division Multiple Access [CDMA], and Integrated Digital Enhanced Network [IDEN]).
The communication sub-module 562 may comprise a plurality of size, topology, traffic control mechanism and organizational intent. The communication sub-module 562 may comprise a plurality of embodiments, such as, but not limited to:
The aforementioned network may comprise a plurality of layouts, such as, but not limited to, bus network such as ethernet, star network such as Wi-Fi, ring network, mesh network, fully connected network, and tree network. The network can be characterized by its physical capacity or its organizational purpose. Use of the network, including user authorization and access rights, differ accordingly. The characterization may include, but not limited to nanoscale network, Personal Area Network (PAN), Local Area Network (LAN), Home Area Network (HAN), Storage Area Network (SAN), Campus Area Network (CAN), backbone network, Metropolitan Area Network (MAN), Wide Area Network (WAN), enterprise private network, Virtual Private Network (VPN), and Global Area Network (GAN).
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the sensors sub-module 563 as a subset of the I/O 560. The sensors sub-module 563 comprises at least one of the devices, modules, and subsystems whose purpose is to detect events or changes in its environment and send the information to the computing device 500. Sensors are sensitive to the measured property, are not sensitive to any property not measured, but may be encountered in its application, and do not significantly influence the measured property. The sensors sub-module 563 may comprise a plurality of digital devices and analog devices, wherein if an analog device is used, an Analog to Digital (A-to-D) converter must be employed to interface the said device with the computing device 500. The sensors may be subject to a plurality of deviations that limit sensor accuracy. The sensors sub-module 563 may comprise a plurality of embodiments, such as, but not limited to, chemical sensors, automotive sensors, acoustic/sound/vibration sensors, electric current/electric potential/magnetic/radio sensors, environmental/weather/moisture/humidity sensors, flow/fluid velocity sensors, ionizing radiation/particle sensors, navigation sensors, position/angle/displacement/distance/speed/acceleration sensors, imaging/optical/light sensors, pressure sensors, force/density/level sensors, thermal/temperature sensors, and proximity/presence sensors. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting examples of the aforementioned sensors:
Chemical sensors, such as, but not limited to, breathalyzer, carbon dioxide sensor, carbon monoxide/smoke detector, catalytic bead sensor, chemical field-effect transistor, chemiresistor, electrochemical gas sensor, electronic nose, electrolyte-insulator-semiconductor sensor, energy-dispersive X-ray spectroscopy, fluorescent chloride sensors, holographic sensor, hydrocarbon dew point analyzer, hydrogen sensor, hydrogen sulfide sensor, infrared point sensor, ion-selective electrode, nondispersive infrared sensor, microwave chemistry sensor, nitrogen oxide sensor, olfactometer, optode, oxygen sensor, ozone monitor, pellistor, pH glass electrode, potentiometric sensor, redox electrode, zinc oxide nanorod sensor, and biosensors (such as nano-sensors).
Automotive sensors, such as, but not limited to, air flow meter/mass airflow sensor, air-fuel ratio meter, AFR sensor, blind spot monitor, engine coolant/exhaust gas/cylinder head/transmission fluid temperature sensor, hall effect sensor, wheel/automatic transmission/turbine/vehicle speed sensor, airbag sensors, brake fluid/engine crankcase/fuel/oil/tire pressure sensor, camshaft/crankshaft/throttle position sensor, fuel/oil level sensor, knock sensor, light sensor, MAP sensor, oxygen sensor (02), parking sensor, radar sensor, torque sensor, variable reluctance sensor, and water-in-fuel sensor.
Consistent with the embodiments of the present disclosure, the aforementioned computing device 500 may employ the peripherals sub-module 562 as a subset of the I/O 560. The peripheral sub-module 565 comprises ancillary devices used to put information into and get information out of the computing device 500. There are 3 categories of devices comprising the peripheral sub-module 565, which exist based on their relationship with the computing device 500, input devices, output devices, and input/output devices. Input devices send at least one of data and instructions to the computing device 500. Input devices can be categorized based on, but not limited to:
Output devices provide output from the computing device 500. Output devices convert electronically generated information into a form that can be presented to humans. Input/output devices that perform both input and output functions. It should be understood by a person having ordinary skill in the art that the ensuing are non-limiting embodiments of the aforementioned peripheral sub-module 565:
Human Interface Devices (HID), such as, but not limited to, pointing device (e.g., mouse, touchpad, joystick, touchscreen, game controller/gamepad, remote, light pen, light gun, Wii remote, jog dial, shuttle, and knob), keyboard, graphics tablet, digital pen, gesture recognition devices, magnetic ink character recognition, Sip-and-Puff (SNP) device, and Language Acquisition Device (LAD).
High degree of freedom devices, that require up to six degrees of freedom such as, but not limited to, camera gimbals, Cave Automatic Virtual Environment (CAVE), and virtual reality systems.
Video Input devices are used to digitize images or video from the outside world into the computing device 500. The information can be stored in a multitude of formats depending on the user's requirement. Examples of types of video input devices include, but not limited to, digital camera, digital camcorder, portable media player, webcam, Microsoft Kinect, image scanner, fingerprint scanner, barcode reader, 3D scanner, laser rangefinder, eye gaze tracker, computed tomography, magnetic resonance imaging, positron emission tomography, medical ultrasonography, TV tuner, and iris scanner.
Audio input devices are used to capture sound. In some cases, an audio output device can be used as an input device, in order to capture produced sound. Audio input devices allow a user to send audio signals to the computing device 500 for at least one of processing, recording, and carrying out commands. Devices such as microphones allow users to speak to the computer in order to record a voice message or navigate software. Aside from recording, audio input devices are also used with speech recognition software. Examples of types of audio input devices include, but not limited to microphone, Musical Instrument Digital Interface (MIDI) devices such as, but not limited to a keyboard, and headset.
Data Acquisition (DAQ) devices convert at least one of analog signals and physical parameters to digital values for processing by the computing device 500. Examples of DAQ devices may include, but not limited to, Analog to Digital Converter (ADC), data logger, signal conditioning circuitry, multiplexer, and Time to Digital Converter (TDC).
Output Devices may further comprise, but not be limited to:
Input/Output Devices may further comprise, but not be limited to, touchscreens, networking device (e.g., devices disclosed in network 562 sub-module), data storage device (non-volatile storage 561), facsimile (FAX), and graphics/sound cards.
All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
While the specification includes examples, the disclosure's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as examples for embodiments of the disclosure.
Insofar as the description above and the accompanying drawing disclose any additional subject matter that is not within the scope of the claims below, the disclosures are not dedicated to the public and the right to file one or more applications to claims such additional disclosures is reserved.
| Number | Date | Country | |
|---|---|---|---|
| 63533049 | Aug 2023 | US |
