Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 and 12-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 recite a method (process) and Claims 12-20 recite a method (process) and therefore fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-20, the claims are directed to a manner of determining metrics for a tenant based on the analysis of collected information, which under its broadest reasonable interpretation covers concepts covered under the Mental Processes grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
A [computer-implemented] method of monitoring rental history,
Claim 12 (A method of monitoring rental history,)
the method comprising: receiving data of a rental history for a tenant, the data of the rental history for the tenant including at least two data sets selected from past rent owed, payment history, length of time at a previous residence, history of the tenant being a primary renter, history of the tenant being a co- tenant, history of the tenant being a co-signer, or occurrences of judgments against the tenant;
assigning a category score to each data set of the at least two data sets [using a convolutional neural network (CNN)]; determining, [by the CNN], an average category score for the at least two data sets by averaging the assigned category scores for each data set of the at least two data sets; generating a rental credit score for the tenant based on the determined average category score for the at least two data sets; [and generating a non-fungible token (NFT) including the rental credit score for the tenant, wherein the NFT is authenticated using a blockchain].
Where the portions not bracketed recite the abstract idea.
Here the claims are directed to concepts capable of being performed in the human mind or via pen and paper (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components. In the present application concepts directed to a manner of determining metrics for a tenant based on the analysis of collected information. (See paragraphs 3-5).
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in the human mind or pen and paper, it falls under the Mental Processes grouping of abstract ideas. See MPEP 2106.04.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The examiner views the following as the additional elements:
A computer. (See paragraph 42)
A CNN. (See paragraph 29)
NFT. (See paragraph 40)
Blockchain. (See paragraph 40)
These additional elements are recited at a high-level of generality such that they act to merely “apply” the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See MPEP 2106.05 (f))
Regarding and generating a non-fungible token (NFT) including the rental credit score for the tenant, wherein the NFT is authenticated using a blockchain the examiner views these limitations as results oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this limitation is viewed as equivalent to “apply it” for merely implementing the abstract idea. (See Id. and paragraph 40)
The combination of these additional elements and/or results oriented steps are no more than mere instructions to apply the exception using generic computing components. (See MPEP 2106.05 (f)) Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?):
As noted above, the claims as a whole merely describes a method that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
Dependent claims 2-8 further define the abstract idea as identified. Additionally, the claim recites the generic CNN (See paragraph 29) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 2-8 are considered to be patent ineligible.
Dependent claim 9-10 and 13-20 further defines the abstract idea as identified. Therefore claim 9-10 and 13-20 are considered to be patent ineligible.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Metev et al. (US 20210201391) in view of Rose et al. (US 20230080599).
Referring to claims 1 and 12,
Metev, which is directed to rental property monitoring, teaches
A computer-implemented method of monitoring rental history, Claim 12 (A method of monitoring rental history,) the method comprising: receiving data of a rental history for a tenant, the data of the rental history for the tenant including at least two data sets selected from past rent owed, payment history, length of time at a previous residence, history of the tenant being a primary renter, history of the tenant being a co- tenant, history of the tenant being a co-signer, or occurrences of judgments against the tenant; (Metev paragraph 26 teaching in addition to protection of rental properties via data from cameras and other sensors, disclosed embodiments utilize information about prospective renters and booking circumstances to analyze a prospective renter perform action sequences which can include approving a prospective renter, denying a prospective renter, and/or suggesting alternative properties to the prospective renter. Metev paragraph 27 teaching FIG. 1 is a flowchart 100 showing phases of embodiments of the present invention, indicating three phases of property protection. At 110, phase 1 is shown, of pre-booking analysis. This can include examining previous booking history of a prospective renter (if available), as well as other rental criteria. At 120, phase 2 is shown, of booking period analysis. During booking period analysis, the premises are monitored via one or more cameras, audio level sensors, and/or other environmental sensors, including, but not limited to, temperature sensors, light sensors, smoke sensors and/or humidity sensors. At 130, phase 3 is shown, of post-reservation data aggregation. Once a person has completed a stay at a property, an account and/or renter profile is updated with information including any incidents such as complaints of loud noise, property damage, rule violations, and the like. The renter profile and/or booking history is used as part of the criteria for pre-booking analysis. The account/renter profile also may include a mobile device (e.g., telephone) associated with the guest, address, payment info, reviews, reputation and other personal information. Metev paragraphs 31-34 teaching at 224, a risk assessment algorithm is used to determine the risk profile 226 of the guest. In embodiments, a risk score is computed based on information in the risk profile 226 based on a mathematical formula. In response to a risk score exceeding a predetermined value, a specific action may be taken such as a request for additional information from the guest at 228, a reservation denial, and/or specific guest-tailored recommendations 230. In one embodiment, a risk score S is computed by: S=K1(F(d))+K2(F(h))+K3(F(c)) K4(F(a))+K5(F(n))+K6(F(w))+K7(F(s)+K8(F(alg)), where:
F(d) is a function of distance from tenant's permanent residence to the rental property location; F(h) is a function of renter history; and F(c) is a function of reservation cost.F(a) is a function of renter age group F(n) is a function of the number of guests confirmed on the reservation F(w) is a function of the day of the week/holiday for which the reservation is confirmed F(s) is a function of the size of the property and the max number of guests it can hostF(alg) is a function built by a machine learning algorithm that evaluates various criteria
K1, K2, through Kn are constants used for fine-tuning the formula. In embodiments, a higher score S indicates a higher risk. In embodiments, F(d) varies inversely to the value d. Thus, when a renter attempts to rent a property that is very close to his/her permanent residence, F(d) has a high value, elevating the score S. In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 55 teaching system 600 may further include an account database 636. The account database 636 may comprise multiple records, where each record includes entities such as host records, tenant records, property records, and event records. The account database 636 may be implemented as a relational database, utilizing a Structured Query Language (SQL) format, or another suitable database format. Using such a relational database, an event record represents a particular reservation of a property, and is associated with a property record, a host record, and a tenant record. The Examiner views Metev utilizes a plurality of information in assessing whether to approve a prospective renter including personal information and prior stay experiences. The Examiner interprets that Metev suggests the computation of the renter score based on a variety of parameters and information associated with the renter including personal information and past stay information )
assigning a category score to each data set of the at least two data sets using a convolutional neural network (CNN); (Metev paragraph 27 teaching FIG. 1 is a flowchart 100 showing phases of embodiments of the present invention, indicating three phases of property protection. At 110, phase 1 is shown, of pre-booking analysis. This can include examining previous booking history of a prospective renter (if available), as well as other rental criteria. At 120, phase 2 is shown, of booking period analysis. During booking period analysis, the premises are monitored via one or more cameras, audio level sensors, and/or other environmental sensors, including, but not limited to, temperature sensors, light sensors, smoke sensors and/or humidity sensors. At 130, phase 3 is shown, of post-reservation data aggregation. Once a person has completed a stay at a property, an account and/or renter profile is updated with information including any incidents such as complaints of loud noise, property damage, rule violations, and the like. The renter profile and/or booking history is used as part of the criteria for pre-booking analysis. The account/renter profile also may include a mobile device (e.g., telephone) associated with the guest, address, payment info, reviews, reputation and other personal information. Metev paragraph 34 teaching in embodiments, a higher score S indicates a higher risk. In embodiments, F(d) varies inversely to the value d. Thus, when a renter attempts to rent a property that is very close to his/her permanent residence, F(d) has a high value, elevating the score S. In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
determining, by the CNN, an average category score for the at least two data sets by averaging the assigned category scores for each data set of the at least two data sets; (Metev paragraph 34 teaching in embodiments, a higher score S indicates a higher risk. In embodiments, F(d) varies inversely to the value d. Thus, when a renter attempts to rent a property that is very close to his/her permanent residence, F(d) has a high value, elevating the score S. In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
generating a rental credit score for the tenant based on the determined average category score for the at least two data sets. (Metev paragraph 31 teaching at 224, a risk assessment algorithm is used to determine the risk profile 226 of the guest. In embodiments, a risk score is computed based on information in the risk profile 226 based on a mathematical formula. In response to a risk score exceeding a predetermined value, a specific action may be taken such as a request for additional information from the guest at 228, a reservation denial, and/or specific guest-tailored recommendations 230. Metev paragraph 34 teaching in embodiments, a higher score S indicates a higher risk. In embodiments, F(d) varies inversely to the value d. Thus, when a renter attempts to rent a property that is very close to his/her permanent residence, F(d) has a high value, elevating the score S. In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching the output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence.)
Metev does not teach or suggest and generating a non-fungible token (NFT) including the rental credit score for the tenant, wherein the NFT is authenticated using a blockchain.
However Rose, which is directed to implementing a tokenized contract on a blockchain, teaches and generating a non-fungible token (NFT) including the rental credit score for the tenant, wherein the NFT is authenticated using a blockchain. (Rose paragraph 28 teaching these assets 100 include fungible physical assets, such as a dollar bill 105 and fungible digital assets, such as a crypto coin 110. The assets 100 also include non-fungible physical assets, such as a house 115, and non-fungible digital assets, such as a non-fungible token NFT 120. As discussed previously, an NFT is a one-of-a-kind digital item or token that cannot be replaced or interchanged with another item. The NFT 120 is stored on a blockchain's digital ledger, as shown by FIG. 2. Rose paragraph 65 teaching by tokenizing credit reports and scores into NFTs, the disclosed embodiments can also facilitate a reverse marketplace, as shown by the reverse marketplace 1000 of FIG. 10. With a reverse marketplace 1000, a user 1005 can submit his/her tokenized credit information (e.g., credit information that has been minted into an NFT), and multiple suppliers (e.g., suppliers 1010, 1015, 1020, 1025, 1030, and 1035) can attempt to underbid one another to perform services for the user 1005.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determination and presentation of a risk score associated with a renter as taught in Metev to incorporate and generating a non-fungible token (NFT) including the rental credit score for the tenant, wherein the NFT is authenticated using a blockchain as taught in Rose with the motivation of leveraging blockchain technology to prevent the modification or tampering of the risk score through the utilization of NFTs. (Rose paragraph 28)
Referring to claims 2 and 13,
Metev further teaches weighting, by the CNN, the determined average score to generate a weighted rental credit score for the tenant. (Metev paragraph 34 teaching in embodiments, a higher score S indicates a higher risk. In embodiments, F(d) varies inversely to the value d. Thus, when a renter attempts to rent a property that is very close to his/her permanent residence, F(d) has a high value, elevating the score S. In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 3 and 14,
Metev further teaches identifying, by the CNN, a time factor for the category score for at least one data set of the at least two data sets; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
and modifying the assigned category score for the at least one data set of the at least two data sets based on the time factor. (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 4-5 and 15-16,
Metev further teaches assigning, by the CNN, a behavior score for the tenant based on the behavior of the tenant at the previous residence; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
and modifying the weighted rental credit score based on the behavior score. (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 6 and 17,
Metev further teaches assigning, by the CNN, a weight to the category score for each data set of the at least two data sets; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
identifying, by the CNN, a time factor for the category score for at least one data set of the at least two data sets; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
modifying, by the CNN, the weight assigned to the category score for each data set of the at least two data sets based on the identified time factor; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
and generating a weighted rental credit score for the tenant based on the modified weight assigned to the category score for each data set of the at least two data sets. (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 7 and 18,
Metev further teaches weighting, by the CNN, the category scores assigned to each data set of the at least two data sets; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
and generating, by the CNN, a weighted rental credit score for the tenant based on a sum of the weighted category scores assigned to each data set of the at least two data sets. (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 8 and 19,
Metev further teaches receiving updated data of the rental history for the tenant, the updated data of the rental history for the tenant including at least two updated data sets having data selected from past rent owed, payment history, length of time at at least one previous residence, history of the tenant being a primary renter, history of the tenant being a co-tenant, history of the tenant being a co- signer, occurrences of judgments against the tenant, or behavior of the tenant at the at least one previous residence; (Metev paragraph 27 teaching once a person has completed a stay at a property, an account and/or renter profile is updated with information including any incidents such as complaints of loud noise, property damage, rule violations, and the like. The renter profile and/or booking history is used as part of the criteria for pre-booking analysis. Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
assigning, by the CNN, an updated category score to each updated data set of the at least two updated data sets; (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
determining, by the CNN, an updated average category score for the at least two updated data sets by averaging the assigned updated category scores for each updated data set of the at least two updated data sets; (Metev paragraph 34 teaching
In embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
and generating an updated rental credit score for the tenant based on the determined average updated category score for the at least two updated data sets. (Metev paragraph 34 teaching in embodiments, F(h) may be a function of a number of previous incidents and optionally, the severity of each incident. A higher number of incidents and higher severities of incidents increases the value of F(h) which also increases the risk score S. As an example, a complaint of loud noises from a renter may be deemed a minor incident having a value of one. In contrast, causing property damage at a property location may be deemed a major incident having a value of ten. In some embodiments, over time, older incidents may be deleted. Thus, over time, a renter can improve their booking history with a series of incident-free reservations. In embodiments, F(c) may vary with reservation cost. As reservation cost increases, the value of F(c) increases, contributing to the overall risk score S. The aforementioned formula is exemplary, and other formulas utilizing linear and/or non-linear relationships may be used instead of, or in addition to, the aforementioned formula. In some embodiments, the scores may be generated by a machine learning system that has been trained using supervised and/or unsupervised learning techniques. Metev paragraph 49 teaching FIG. 5 is a block diagram 500 indicating details of layer 3 video analysis. This is used for reservation period analysis (120 of FIG. 1). Input from layer 2 processing (404 of FIG. 4) is combined with reservation data and guest specific data 504 that comes from reservation engine 502. Layer 3 processing includes an anomaly detector that is powered by machine learning. In embodiments, the machine learning can include, but is not limited to, may include a neural network, convolutional neural network (CNN), Decision Trees, Random Forests, clustering, hierarchical clustering, k-means, and/or any other supervised learning techniques, unsupervised learning techniques, or a combination of both supervised and unsupervised learning techniques. In embodiments, TensorFlow or other suitable frameworks may be used in the implementation of machine learning systems used with disclosed embodiments. The output of the layer 3 video analysis can include a risk score, processed video, annotated video, audio level information, guest information, residence information, and/or other information that can be used by stakeholders to make decisions regarding the residence. Metev paragraph 56 teaching system 600 may further include a machine learning system 618. Machine learning system 618 may be used to further categorize and classify input data including data acquired from IoT sensors, image data, scenery, object recognition and/or object classification, person recognition, natural language processing (NLP), sentiment analysis, and/or other classification processes. Machine learning system 618 may include one or more neural networks, convolutional neural networks (CNNs), and/or other deep learning techniques. The machine learning system 618 may include regression algorithms, classification algorithms, clustering techniques, anomaly detection techniques, Bayesian filtering, and/or other suitable techniques to analyze the information obtained by the reservation property protection server 602 to assist in assessing threats to the property based on tenant/guest activity and/or previous booking history.)
Referring to claims 9-10 and 20,
Metev further teaches including transmitting the updated rental credit score to a lender or insurance provider. (Metev paragraph 31 teaching at 224, a risk assessment algorithm is used to determine the risk profile 226 of the guest. In embodiments, a risk score is computed based on information in the risk profile 226 based on a mathematical formula. In response to a risk score exceeding a predetermined value, a specific action may be taken such as a request for additional information from the guest at 228, a reservation denial, and/or specific guest-tailored recommendations 230. Metev paragraph 77 teaching it extracts actionable data about people count, movement, mood, needs, behavior and carried objects from security video. The same algorithm analyses multiple data points about our guests, including our written communication with them, to establish their risk profile and recommends preventive or proactive actions whenever needed.)
Response to Arguments
Applicant's arguments filed December 4, 2025 have been fully considered.
Applicant’s amendments and arguments, on page 9 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues generating a rental credit score provides a practical application of any alleged abstract idea by generating a renter-specific credit score embedded in an NFT. According to Applicant the claims provide a specific and limited set of data points to be relied upon in generating the rental credit score, and the method applies those defined inputs through a structured sequence of operations that transform the data into a non-fungible, verifiable token. Therefore, the claims apply the recited steps in a manner that affects a concrete transformation of data into a distinct digital artifact, demonstrating a practical application rather than a mere abstract concept.
The Examiner respectfully disagrees viewing the collection and analysis of the renter information to generate rental credit score are a part of the recited abstract idea. Regarding the machine or transformation test the Examiner first notes MPEP 2106 and MPEP 2106.04(d)
The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework. Likewise, eligibility should not be evaluated based on whether the claim recites a "useful, concrete, and tangible result," State Street Bank, 149 F.3d 1368, 1374, 47 USPQ2d 1596, 1602 (Fed. Cir. 1998) (quoting In re Alappat, 33 F.3d 1526, 1544, 31 USPQ2d 1545, 1557 (Fed. Cir. 1994))
In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP §§ 2106.05(b), 2106.05(c), and 2106.05(f).
Here the Examiner viewed the generation of the NFT including the rental credit score is recited with a lack of specificity such that it and amounts to mere instructions to apply the abstract idea.
Therefore the Examiner has maintained the 101 rejection.
Applicant’s amendments and arguments regarding the 102 rejection, on pages 10-13 the Examiner finds the amendments persuasive. Therefore the Examiner has withdrawn the 102 rejection.
Applicant’s amendments and arguments regarding the 103 rejection, on pages 10-13 the Examiner finds unpersuasive.
Applicant argues that the Metev reference only teaches the use of behavioral factors to determine a risk. (Metev paragraphs 22, 27, 32, and 48- 49) According to Applicant, the claims no longer recite behavior factors and thus the prior art does not teach or suggest amended claims 1 and 12. Applicant contends that the prior art of record teaches generating a non-fungible token that includes the specific rental credit score produced according to the amended claims. According to Applicant, Metev generates a behavior incident based risk score, while the amended claims recite a rental-history based credit score derived from the defined rental history data sets using the recited CNN. Applicant further contends that Rose does not teach modifying the underlying scoring process of another system or applying its blockchain-authenticated NFT techniques to a rental-history based credit scored as claimed.
The Examiner respectfully disagrees viewing the portions of Metev cited in response to Applicant’s amendments suggests receipt of a prospective renter’s information regarding a prospective’s history and account information in determining the rental score as claimed by Applicant. The Examiner views that the Rose provides for storing score information utilizing blockchain and NFTs such that one of ordinary skill in the art would be motivated to modify the generated score in Metev to be included in a generated NFT that is authenticated using a blockchain for preventing modification of the determined score as taught by Rose.
Therefore the Examiner has maintained the 103 rejection and withdrawn the 102 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sakurai et al. (US 20230377069) -directed to supporting purchase and sale of real estate.
Jethmalani et al. (US 20230298001) -directed to NFT purchase and transfer.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the - examiner should be directed to MICHAEL J MONAGHAN whose telephone number is (571)270-5523. The examiner can normally be reached on Monday- Friday 8:30 am - 5:30 pm.
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/M.J.M./
Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629