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 1-21 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-11 recite a system (machine), Claims 12-20 recite a method (process) and Claim 21 recites a non-transitory machine-readable media (manufacture) and therefore fall into a statutory category. The Examiner is interpreting the method and media perform the functions of the system for examination purposes.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-21, the claims recite concepts covering a manner of determining scores related to a property and recommendations for improving the scores, which under its broadest reasonable interpretation, covers concepts under the Mental Processes and Certain Methods of Organizing Human Activity grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
(Claim 1)
[A smart home computer system for] evaluating and mitigating aspects of a residential property, [the smart home computer system configured to coordinate a connected home ecosystem and comprising: a home controller installed within the residential property; and a remote system server configured to communicate with the home controller and one or more external data sources outside the residential property via an external network, and (ii) execute a smart home analysis machine learning model, the remote system server comprising one or more processors programmed to:]
(Claim 12)
[A computer-implemented] method for evaluating and mitigating aspects of a residential property, the [computer-implemented] method implemented [by a smart home computer device configured to coordinate a connected home ecosystem and comprising one or more processors in communication with one or more memory devices], wherein the [computer-implemented] method comprises:
(Claim 21)
[At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by a smart home computing device configured to coordinate a connected home ecosystem and comprising at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:]
receive a first element of home data [from the home controller];
receive a first element of external data [from the one or more external data sources];
convert the first element of external data to a format compatible for input [in the smart home analysis machine learning model];
output, [from the smart home analysis machine learning model], a home health score for the residential property based at least in part on one or more of the first element of home data and the first element of external data [from the one or more external data sources], the home health score representing a measure of health of the residential property;
select, based upon the home health score, one or more candidate products and services listed in a digital marketplace associated with the connected home ecosystem, the one or more candidate products and services determined to be able to improve the home health score;
and cause to be displayed, to a homeowner of the residential property [via a graphical user interface of a user device of the homeowner], information about the one or more selected candidate products and services.
Where the portions not bracketed recite the abstract idea
Here the claims recite concepts covering Mental Processes (including an observation, evaluation, judgement, opinion) but for the recitation of generic computer components. In the present application concepts reciting a manner of determining scores related to a property based on the analysis of collected information. (See paragraphs 51-55). Additionally, the claims recite concepts covering Certain Methods of Organizing Human Activity in particular managing personal behavior or relationships or interactions between people (following rules or instructions) but for the recitation of generic computer components. In the present application concepts reciting a manner of determining recommendations to provide a user to improve scores related to a property. (See paragraphs 51-55).
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in the human mind or via pen and paper falls under the Mental Processes grouping of abstract ideas. See MPEP 2106.04. If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in managing personal behavior or relationships or interactions between people it falls under the Certain Method of Organizing Human Activity grouping of abstract ideas. See Id.
Accordingly, the claims recite an abstract idea.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The examiner views the following as the additional elements:
A smart home computer system. (See Figure 9 and paragraph 172)
A home controller. (See paragraphs 63, 76, and 173)
A remote server. (See paragraph 173)
One or more external data sources. (See paragraphs 71-72)
An external network. (See paragraph 68)
One or more processors. (See paragraph 7)
Computer-implemented. (See paragraph 9)
A computing device. (See paragraph 8)
One or more memory devices. (See paragraph 180)
At least one non-transitory computer-readable media. (See paragraphs 180 and 216)
Computer-executable instructions. (See paragraphs 180 and 186)
A graphical user interface. (See paragraph 182)
A connected home ecosystem. (See paragraph 76 and Figure 3)
A smart home analysis machine learning model. (See paragraph 218)
A user device. (See paragraph 174)
A digital marketplace. (See paragraph 6)
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 “coordinate a connected home ecosystem” and “execute a smart home analysis machine learning model” the Examiner views as results oriented solution steps lacking details that amounts to mere instructions to apply the abstract idea using generic computing components and does integrate the abstract idea into a practical application. (See Id. and paragraphs 49 and 217)
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 Id.) 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 and system 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 and 13 recite the additional elements of the generic one or more smart devices installed within the residential property (See paragraphs 76) and home controller (See paragraphs 63, 76, and 173), at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Additionally, the claims recite results-oriented solution step of “wherein the one or more smart devices are configured to control one or more systems of the residential property” (See paragraphs 76 and 98) and is therefore viewed as equivalent to mere instructions to apply 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 and 13 are considered to be patent ineligible.
Dependent claims 3 and 14 further define the abstract idea as identified. Additionally, the claim recites the additional elements of generic home controller (See paragraphs 63, 76, and 173), one or more smart devices (See paragraph 76), a home network (See paragraphs 68-69), and the connected home ecosystem (See paragraphs 63, 76, and 173) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 3 and 14 are considered to be patent ineligible.
Dependent claims 4 and 15 further define the abstract idea as identified. Additionally, the claim recites the additional elements of the generic one or more smart devices (See paragraphs 76) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 4 and 15 are considered to be patent ineligible.
Dependent claims 5 and 16 further define the abstract idea as identified. Additionally, the claims recite the additional elements of generic one or more processors (See paragraph 7), and graphical user interface (See paragraph 182) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 5 and 16 are considered to be patent ineligible.
Dependent claims 6 and 17 further define the abstract idea as identified. Additionally, the claims recite the additional elements of generic one or processors (See paragraph 7) and smart home analysis machine learning model (See paragraph 218) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. The claims recite the results-oriented solution steps of “the smart home analysis machine learning model trained to match products and services listed in the digital marketplace to home health scores based upon a relevance threshold” (See paragraphs 76 and 98) and are therefore viewed as equivalent 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 6 and 17 are considered to be patent ineligible.
Dependent claims 7 and 18 further recite the results-oriented solution steps of “wherein the one or more processors are further programmed to: receive, from the user device of the homeowner of the residential property, an instruction for a first smart device of the one or more smart devices of the residential property; and transmit the instruction to the home controller, wherein the home controller is configured to transmit the instruction to the first smart device, wherein the first smart device executes the instruction.” (See paragraph 98) and are therefore viewed as equivalent 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 7 and 18 are considered to be patent ineligible.
Dependent claims 8 and 19 further define the abstract idea as identified. Therefore claims 8 and 19 are considered to be patent ineligible.
Dependent claims 9 and 20 further define the abstract idea as identified. Additionally, the claims recite the additional elements of generic one or more processors (See paragraph 7), and graphical user interface (See paragraph 182), and smart home analysis machine learning model (See paragraph 218) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claims 9 and 20 are considered to be patent ineligible.
Dependent claim 10 further defines the abstract idea as identified. Additionally, the claims recite the additional elements of generic one or more processors (See paragraph 7), home controller (See paragraphs 63, 76, and 173) and graphical user interface (See paragraph 182) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 10 is considered to be patent ineligible.
Dependent claim 11 further defines the abstract idea as identified. Additionally, the claims recite the additional elements of generic one or more processors (See paragraph 7) and a third-party server (See paragraph 87) at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 11 is 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-17, and 19-21 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Linn (US 20220366507).
Referring to claims 1, 12, and 21,
Linn, which is directed to automated optimization of the health of a residence, discloses
(Claim 1) [A smart home computer system for] evaluating and mitigating aspects of a residential property, [the smart home computer system configured to coordinate a connected home ecosystem and comprising: a home controller installed within the residential property; and a remote system server configured to communicate with the home controller and one or more external data sources outside the residential property via an external network, and (ii) execute a smart home analysis machine learning model, the remote system server comprising one or more processors programmed to:]
(Claim 12) A computer-implemented method for evaluating and mitigating aspects of a residential property, the computer-implemented method implemented by a smart home computer device configured to coordinate a connected home ecosystem and comprising one or more processors in communication with one or more memory devices, wherein the computer-implemented method comprises:
(Claim 21) At least one non-transitory computer-readable media having computer-executable instructions embodied thereon, wherein when executed by a smart home computing device configured to coordinate a connected home ecosystem and comprising at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to: (Linn paragraph 65 disclosing turning to FIG. 1, the primary functionality of the VFM system 100 is embodied in a VFM Server 110 that communicates over the Internet 105 with integrated networks of (i) providers, including Service Providers 130 and Other Providers 140 (eg, equipment vendors, insurance and warranty providers, etc.) and (ii) users (including homeowners, business owners and other users) from their desktop, laptop and mobile devices 160 and from their Home and Business Premises 150. The VFM system 100 also integrates with External Data Sources 128, for example, to obtain environmental data, such as weather data, as well as air, water and soil quality data and event data (eg, regarding recent earthquakes, floods or disease outbreaks). Linn paragraph 94 disclosing in one embodiment, an on-site Premises Controller 158 filters and processes data from Sensor Network 156 before delivering it over the Internet 105 to VFM Server 110. Premises Controller 158 filters such data in part to address the impracticality of sending to VFM Server 110 the entirety of the raw data generated by every sensor. Linn paragraph 107 disclosing moreover, to the extent that functional modules within VFM Server 110 (or those included in Premises Controller 158 or in provider or other external servers) are implemented in software, such software is embodied in physical non-transitory computer-accessible storage media (i.e., memory) from which it is invoked for execution by one or more CPUs or other physical processing units. Linn paragraph 125 teaching turning to FIGS. 3A, 3B and 3C, the functionality of one embodiment of the prediction engines is now described. VFM Server 110 integrates three distinct prediction engines (Scoring Engine 118, Alert Generation Engine 120 and Action and Goals Optimization Engine 122), each of which is implemented as a machine-learning-based neural network that employs predictive techniques to assess likely outcomes. In other words, given a large set of inputs, the combination of which has very likely not been encountered before, each of these prediction engines assesses and “predicts” the most probable outcomes.)
receive a first element of home data from the home controller; (Linn paragraph 65 disclosing turning to FIG. 1, the primary functionality of the VFM system 100 is embodied in a VFM Server 110 that communicates over the Internet 105 with integrated networks of (i) providers, including Service Providers 130 and Other Providers 140 (eg, equipment vendors, insurance and warranty providers, etc.) and (ii) users (including homeowners, business owners and other users) from their desktop, laptop and mobile devices 160 and from their Home and Business Premises 150. The VFM system 100 also integrates with External Data Sources 128, for example, to obtain environmental data, such as weather data, as well as air, water and soil quality data and event data (eg, regarding recent earthquakes, floods or disease outbreaks). Linn paragraph 94 disclosing in one embodiment, an on-site Premises Controller 158 filters and processes data from Sensor Network 156 before delivering it over the Internet 105 to VFM Server 110. Premises Controller 158 filters such data in part to address the impracticality of sending to VFM Server 110 the entirety of the raw data generated by every sensor. Linn paragraph 108 disclosing
As noted above, Premises Controller 158 continuously processes raw data from the sensors via local sensor network 156 at the premises 150. After filtering and converting this raw data as described above, Premises Controller 158 streams this processed sensor data to Data Monitor 115 in VFM Server 110.)
receive a first element of external data from the one or more external data sources;
(Linn paragraph 65 disclosing turning to FIG. 1, the primary functionality of the VFM system 100 is embodied in a VFM Server 110 that communicates over the Internet 105 with integrated networks of (i) providers, including Service Providers 130 and Other Providers 140 (eg, equipment vendors, insurance and warranty providers, etc.) and (ii) users (including homeowners, business owners and other users) from their desktop, laptop and mobile devices 160 and from their Home and Business Premises 150. The VFM system 100 also integrates with External Data Sources 128, for example, to obtain environmental data, such as weather data, as well as air, water and soil quality data and event data (eg, regarding recent earthquakes, floods or disease outbreaks). Linn paragraphs 86-87 disclosing in one embodiment, Home Health Record 126 includes static as well as dynamic information. For example, it contains the home's geographic location and identification of installed equipment and infrastructure, including its location (e.g., room or more precise position) within the home. It also includes user profile information, such as an occupant's ability and/or desire to perform certain troubleshooting tasks themselves. The Home Health Record 126 further includes operational models, performance characteristics and specifications (in certain cases specific to the home's geographic location and environment) for each installed item of equipment, as well as for comparable units, such as those that are newer and more energy-efficient and others that may be smaller or larger alternatives from a systemic perspective. In one embodiment, Home Health Record 126 includes information regarding home infrastructure that is not specific to any item of equipment, such as the thermal rating of windows. Linn paragraph 109 disclosing data Monitor 115 also receives environmental and other external data from a variety of external data sources 128. For example, local weather forecasts provide valuable input that affect predictions of abnormal conditions. Current heavy rain might explain why elevated sump pump usage is not abnormal, while a storm warning might result in preventive charging of batteries in a solar system. Other external environmental data can include air, water and soil quality, as well as major nearby events, such as an earthquake, flood or infectious disease outbreak or pandemic.)
convert the first element of external data to a format compatible for input into the smart home analysis machine learning model; (Linn paragraph 96 teaching moreover, Premises Controller 158 also normalizes the units of raw data among various sensors through a conversion process. As a result, the converted data sent by Premises Controller 158 to VFM Server 110 can be meaningfully compared across different sensors (e.g. to determine relative statistically significant changes over time). Linn paragraphs 108-110 teaching as noted above, Premises Controller 158 continuously processes raw data from the sensors via local sensor network 156 at the premises 150. After filtering and converting this raw data as described above, Premises Controller 158 streams this processed sensor data to Data Monitor 115 in VFM Server 110. Data Monitor 115 also receives environmental and other external data from a variety of external data sources 128. For example, local weather forecasts provide valuable input that affect predictions of abnormal conditions. Current heavy rain might explain why elevated sump pump usage is not abnormal, while a storm warning might result in preventive charging of batteries in a solar system. Other external environmental data can include air, water and soil quality, as well as major nearby events, such as an earthquake, flood or infectious disease outbreak or pandemic. Data Monitor 115 parses this sensor-based and environmental data for each home, and processes and formats the data for input to the prediction engines. It should be noted that the processed data provided to the prediction engines includes timestamped current and historical raw data (in one embodiment), as well as data that have been filtered and converted by Premises Controller 158. Linn paragraph 118 disclosing in step 210, Data Monitor 115 further processes such sensor data, along with historical data from Home Health Record 126 and environmental data (e.g., from External Data Sources 128), and parses such data into a format suitable for input to the prediction engines. In step 215, Data Monitor 115, in conjunction with Home Health Record Generator 116, updates Home Health Record 126 to reflect the new data since the prior iteration of this process 200.)
output, from the smart home analysis machine learning model, a home health score for the residential property based at least in part upon one or more of the first element of home data and the converted first element of external data from the one or more external data sources, the home health score representing a measure of health of the residential property; (Linn paragraph 68 disclosing VFM Server 110 employs Prediction Engine Manager 117 to manage a set of prediction engines to generate alerts and corresponding actions, as described in greater detail below. In one embodiment, these prediction engines include Scoring Engine 118 (to manage scores and subscores representing systemic states of an individual premises, as well as component systems, equipment and infrastructure), Alert Generation Engine 120 (to generate alerts reflecting the occurrence of abnormal conditions) and Action and Goals Optimization Engine 122 (to generate actions corresponding to such alerts in a manner that optimizes user-specified goals). Linn paragraphs 86-87 disclosing in one embodiment, Home Health Record 126 includes static as well as dynamic information. For example, it contains the home's geographic location and identification of installed equipment and infrastructure, including its location (e.g., room or more precise position) within the home. It also includes user profile information, such as an occupant's ability and/or desire to perform certain troubleshooting tasks themselves. The Home Health Record 126 further includes operational models, performance characteristics and specifications (in certain cases specific to the home's geographic location and environment) for each installed item of equipment, as well as for comparable units, such as those that are newer and more energy-efficient and others that may be smaller or larger alternatives from a systemic perspective. In one embodiment, Home Health Record 126 includes information regarding home infrastructure that is not specific to any item of equipment, such as the thermal rating of windows. Linn paragraphs 89-90 disclosing in one embodiment (discussed below), Home Health Record 126 also includes scores reflecting the systemic state of the home itself (as well as sub-scores reflecting equipment systems and individual units or devices). For example, a Reliability score might reflect an overall current state of the reliability of the home (taking into account the reliability over time of individual pieces of equipment). In this embodiment, trendlines of these scores (and sub-scores) are also included in Home Health Record 126. Home Health Record Generator 116 creates and maintains Home Health Record 126 as certain information dynamically changes over time. For example, whenever a new homeowner is added to VFM system 100, Home Health Record Generator 116 creates entries for the various static information relating to that homeowner's residence (e.g., user profile data, geographic data, equipment and infrastructure and related operational data, etc.). Moreover, as dynamic sensor data is received and processed over time, Home Health Record Generator 116 stores and updates Home Health Record 126 to reflect such dynamic data, including alerts, actions and other related data for access by users, providers and various components of VFM system 100. Linn paragraph 96 teaching moreover, Premises Controller 158 also normalizes the units of raw data among various sensors through a conversion process. As a result, the converted data sent by Premises Controller 158 to VFM Server 110 can be meaningfully compared across different sensors (e.g. to determine relative statistically significant changes over time). Linn paragraphs 108-110 teaching as noted above, Premises Controller 158 continuously processes raw data from the sensors via local sensor network 156 at the premises 150. After filtering and converting this raw data as described above, Premises Controller 158 streams this processed sensor data to Data Monitor 115 in VFM Server 110. Data Monitor 115 also receives environmental and other external data from a variety of external data sources 128. For example, local weather forecasts provide valuable input that affect predictions of abnormal conditions. Current heavy rain might explain why elevated sump pump usage is not abnormal, while a storm warning might result in preventive charging of batteries in a solar system. Other external environmental data can include air, water and soil quality, as well as major nearby events, such as an earthquake, flood or infectious disease outbreak or pandemic. Data Monitor 115 parses this sensor-based and environmental data for each home, and processes and formats the data for input to the prediction engines. It should be noted that the processed data provided to the prediction engines includes timestamped current and historical raw data (in one embodiment), as well as data that have been filtered and converted by Premises Controller 158. Linn paragraph 118 disclosing in step 210, Data Monitor 115 further processes such sensor data, along with historical data from Home Health Record 126 and environmental data (e.g., from External Data Sources 128), and parses such data into a format suitable for input to the prediction engines. In step 215, Data Monitor 115, in conjunction with Home Health Record Generator 116, updates Home Health Record 126 to reflect the new data since the prior iteration of this process 200. Linn paragraphs 125-126 disclosing turning to FIGS. 3A, 3B and 3C, the functionality of one embodiment of the prediction engines is now described. VFM Server 110 integrates three distinct prediction engines (Scoring Engine 118, Alert Generation Engine 120 and Action and Goals Optimization Engine 122), each of which is implemented as a machine-learning-based neural network that employs predictive techniques to assess likely outcomes. In other words, given a large set of inputs, the combination of which has very likely not been encountered before, each of these prediction engines assesses and “predicts” the most probable outcomes. In alternative embodiments, the prediction engines are implemented with different forms of unsupervised machine learning, statistical analytics, rules-based heuristics and other techniques (or combinations thereof). In such embodiments, the prediction engines still generate similar outputs in response to similar inputs (as compared with their machine-learning neural network counterparts) without departing from the spirit of the present invention. Linn paragraph 130 disclosing in one embodiment, Scoring Engine 350 a generates sub-scores (e.g., for the reliability of an individual item of equipment) in the process of generating a score 325 a reflecting the state of the home. These sub-scores are also maintained in Home Health Record 126.)
select, based upon the home health score, one or more candidate products and services listed in a digital marketplace associated with the connected home ecosystem, the one or more candidate products and services determined determine at least one product provider and service provider to be able to improve the home health score; (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 160 disclosing in one embodiment, Action and Goals Optimization Engine 350c employs the scores 325a generated by Scoring Engine 350a (among other inputs) to generate actions 322c that facilitate these “win-win” outcomes between homeowners and various providers. For example, the integration of Home Efficiency scores enables “energy utility” providers to better manage energy usage by homeowners (e.g., by offering discounts for lower Home Efficiency scores over time). Similarly, Maintenance scores enable Warranty providers to incentivize more proactive preventive maintenance behavior over time. And Home Reliability scores enable Insurance providers to make much more targeted risk assessments, such as discounts for higher Home Reliability scores over time. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
and cause to be displayed, to a homeowner of the residential property via a graphical user interface of a user device of the homeowner, information about the one or more selected candidate products and services. (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraph 65 teaching turning to FIG. 1, the primary functionality of the VFM system 100 is embodied in a VFM Server 110 that communicates over the Internet 105 with integrated networks of (i) providers, including Service Providers 130 and Other Providers 140 (eg, equipment vendors, insurance and warranty providers, etc.) and (ii) users (including homeowners, business owners and other users) from their desktop, laptop and mobile devices 160 and from their Home and Business Premises 150. Linn paragraph 71 teaching this includes managing the process of acquiring information about the various premises and their owners across the Homecare Network, and leveraging such information to create and manage specific tasks unique to particular premises and/or their owners (as well as other users) via their desktop or laptop computers or mobile devices 160. Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
Referring to claims 2 and 13,
Linn further discloses further comprising one or more smart devices installed within the residential property and in communication with the home controller, wherein the one or more smart devices are configured to control one or more systems of the residential property. (Linn paragraph 10 disclosing As “smart homes” and the “Internet of Things” (“IoT”) have proliferated, so too has the ability to monitor device operation and detect abnormal conditions—particularly with the application of machine learning and other forms of artificial intelligence (“AI”). Yet the primary focus of these smart systems has been on the control and interoperability of connected devices, rather than on the detection of abnormal conditions and the extensive troubleshooting expertise necessary to resolve many such conditions. Linn paragraph 92 disclosing at a typical homeowner's premises 150, VFM system 100 includes a Sensor Network 156 to monitor the various equipment and infrastructure installed in the home, such as Devices 154 in various locations throughout premises 150. It should be emphasized that individual sensors do not necessarily bear a one-to-one relationship with each item of equipment. Linn paragraph 156 disclosing action and Goals Optimization Engine 350 c also takes into account systemic considerations when looking at particular equipment to recommend. For example, additional “smart control” devices may be recommended to provide a homeowner with more control over the operational parameters of certain equipment, enabling the homeowner with a greater ability to satisfy an energy efficiency User Goal. Linn paragraph 163 disclosing other actions 322 c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider.)
Referring to claims 3 and 14,
Linn further discloses wherein the home controller is configured to receive home data from the one or more smart devices via a home network associated with the connected home ecosystem. (Linn paragraph 10 disclosing As “smart homes” and the “Internet of Things” (“IoT”) have proliferated, so too has the ability to monitor device operation and detect abnormal conditions—particularly with the application of machine learning and other forms of artificial intelligence (“AI”). Yet the primary focus of these smart systems has been on the control and interoperability of connected devices, rather than on the detection of abnormal conditions and the extensive troubleshooting expertise necessary to resolve many such conditions. Linn paragraph 92 disclosing at a typical homeowner's premises 150, VFM system 100 includes a Sensor Network 156 to monitor the various equipment and infrastructure installed in the home, such as Devices 154 in various locations throughout premises 150. It should be emphasized that individual sensors do not necessarily bear a one-to-one relationship with each item of equipment Linn paragraph 94 disclosing in one embodiment, an on-site Premises Controller 158 filters and processes data from Sensor Network 156 before delivering it over the Internet 105 to VFM Server 110. Premises Controller 158 filters such data in part to address the impracticality of sending to VFM Server 110 the entirety of the raw data generated by every sensor. Linn paragraph 108 disclosing as noted above, Premises Controller 158 continuously processes raw data from the sensors via local sensor network 156 at the premises 150. After filtering and converting this raw data as described above, Premises Controller 158 streams this processed sensor data to Data Monitor 115 in VFM Server 110. Linn paragraph 156 disclosing action and Goals Optimization Engine 350 c also takes into account systemic considerations when looking at particular equipment to recommend. For example, additional “smart control” devices may be recommended to provide a homeowner with more control over the operational parameters of certain equipment, enabling the homeowner with a greater ability to satisfy an energy efficiency User Goal.)
Referring to claims 4 and 15,
Linn further discloses wherein the one or more smart devices are configured to monitor one or more attributes of the residential property. (Linn paragraph 10 disclosing As “smart homes” and the “Internet of Things” (“IoT”) have proliferated, so too has the ability to monitor device operation and detect abnormal conditions—particularly with the application of machine learning and other forms of artificial intelligence (“AI”). Yet the primary focus of these smart systems has been on the control and interoperability of connected devices, rather than on the detection of abnormal conditions and the extensive troubleshooting expertise necessary to resolve many such conditions. Linn paragraph 92 disclosing at a typical homeowner's premises 150, VFM system 100 includes a Sensor Network 156 to monitor the various equipment and infrastructure installed in the home, such as Devices 154 in various locations throughout premises 150. It should be emphasized that individual sensors do not necessarily bear a one-to-one relationship with each item of equipment. Linn paragraph 92 disclosing at a typical homeowner's premises 150, VFM system 100 includes a Sensor Network 156 to monitor the various equipment and infrastructure installed in the home, such as Devices 154 in various locations throughout premises 150. It should be emphasized that individual sensors do not necessarily bear a one-to-one relationship with each item of equipment Linn paragraph 94 disclosing in one embodiment, an on-site Premises Controller 158 filters and processes data from Sensor Network 156 before delivering it over the Internet 105 to VFM Server 110. Premises Controller 158 filters such data in part to address the impracticality of sending to VFM Server 110 the entirety of the raw data generated by every sensor. Linn paragraph 108 disclosing as noted above, Premises Controller 158 continuously processes raw data from the sensors via local sensor network 156 at the premises 150. After filtering and converting this raw data as described above, Premises Controller 158 streams this processed sensor data to Data Monitor 115 in VFM Server 110. Linn paragraph 156 disclosing action and Goals Optimization Engine 350 c also takes into account systemic considerations when looking at particular equipment to recommend. For example, additional “smart control” devices may be recommended to provide a homeowner with more control over the operational parameters of certain equipment, enabling the homeowner with a greater ability to satisfy an energy efficiency User Goal.)
Referring to claims 5 and 16,
Linn further discloses wherein the one or more processors are further programmed to cause to be displayed, to the homeowner of the residential property via the graphical user interface, information about the one or more attributes of the residential property. (Linn paragraphs 99-100 disclosing Homeowners (and owners of businesses and other types of premises) can also access VFM Server 110 via a voice-enabled and/or web browser interface or custom app 165 on their devices 160, such as mobile devices, or through similar interfaces via their laptop or desktop computers (each of which typically includes standard hardware, firmware and operating system 161). In this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 131 disclosing otherwise, the alert is processed by Prediction Engine Manager 117, which (in one embodiment shown in step 228) provides the predicted alert and associated attributes, along with the scores and other input data from Home Health Record 126, to Action and Goals Optimization Engine 122. In step 230, Action and Goals Optimization Engine 122 generates an action (i.e., the next recommended troubleshooting step, as discussed in greater detail below) that corresponds to the current alert (thus creating a current alert-action pair) and is optimized in accordance with a homeowner's predefined user goals. Linn paragraph 146 disclosing in one embodiment, users may initiate queries to VFM Server 110 (e.g., from a voice-enabled mobile app or web browser interface 165 on a laptop or desktop computer). For example, as noted above, a user might submit a query regarding the relative efficiency of their AC system. Such a query would effectively trigger an alert and related metadata regarding the subject of the alert (e.g., the AC system). In this scenario, Action and Goals Optimization Engine 350 c would generate a “response” action including, for example, a summary of the efficiency of the installed AC system (based upon its historic performance) and perhaps a comparative recommendation for an upgrade to a more efficient competitive alternative product.)
Referring to claims 6 and 17,
Linn further discloses one or more processors are further programmed to: select the one or more candidate products and services based upon an output from the smart home analysis machine learning model, the smart home analysis machine learning model trained to match products and services listed in the digital marketplace to home health scores based upon a relevance threshold. (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraphs 125-126 disclosing turning to FIGS. 3A, 3B and 3C, the functionality of one embodiment of the prediction engines is now described. VFM Server 110 integrates three distinct prediction engines (Scoring Engine 118, Alert Generation Engine 120 and Action and Goals Optimization Engine 122), each of which is implemented as a machine-learning-based neural network that employs predictive techniques to assess likely outcomes. In other words, given a large set of inputs, the combination of which has very likely not been encountered before, each of these prediction engines assesses and “predicts” the most probable outcomes. In alternative embodiments, the prediction engines are implemented with different forms of unsupervised machine learning, statistical analytics, rules-based heuristics and other techniques (or combinations thereof). In such embodiments, the prediction engines still generate similar outputs in response to similar inputs (as compared with their machine-learning neural network counterparts) without departing from the spirit of the present invention. Linn paragraph 160 disclosing in one embodiment, Action and Goals Optimization Engine 350c employs the scores 325a generated by Scoring Engine 350a (among other inputs) to generate actions 322c that facilitate these “win-win” outcomes between homeowners and various providers. For example, the integration of Home Efficiency scores enables “energy utility” providers to better manage energy usage by homeowners (e.g., by offering discounts for lower Home Efficiency scores over time). Similarly, Maintenance scores enable Warranty providers to incentivize more proactive preventive maintenance behavior over time. And Home Reliability scores enable Insurance providers to make much more targeted risk assessments, such as discounts for higher Home Reliability scores over time. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
Referring to claims 8 and 19,
Linn further discloses wherein the home data reflects an aspect of operational quality of one or more assets of the residential property. (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 90 disclosing home Health Record Generator 116 creates and maintains Home Health Record 126 as certain information dynamically changes over time. For example, whenever a new homeowner is added to VFM system 100, Home Health Record Generator 116 creates entries for the various static information relating to that homeowner's residence (e.g., user profile data, geographic data, equipment and infrastructure and related operational data, etc.). Moreover, as dynamic sensor data is received and processed over time, Home Health Record Generator 116 stores and updates Home Health Record 126 to reflect such dynamic data, including alerts, actions and other related data for access by users, providers and various components of VFM system 100.)
Referring to claims 9 and 20,
Linn further discloses wherein the one or more processors are further programmed to: generate, based upon an output from the smart home analysis machine learning model, one or more recommendations determined to be able to improve the home health score for the residential property; (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 107 disclosing moreover, to the extent that functional modules within VFM Server 110 (or those included in Premises Controller 158 or in provider or other external servers) are implemented in software, such software is embodied in physical non-transitory computer-accessible storage media (i.e., memory) from which it is invoked for execution by one or more CPUs or other physical processing units. Linn paragraphs 125-126 disclosing turning to FIGS. 3A, 3B and 3C, the functionality of one embodiment of the prediction engines is now described. VFM Server 110 integrates three distinct prediction engines (Scoring Engine 118, Alert Generation Engine 120 and Action and Goals Optimization Engine 122), each of which is implemented as a machine-learning-based neural network that employs predictive techniques to assess likely outcomes. In other words, given a large set of inputs, the combination of which has very likely not been encountered before, each of these prediction engines assesses and “predicts” the most probable outcomes. In alternative embodiments, the prediction engines are implemented with different forms of unsupervised machine learning, statistical analytics, rules-based heuristics and other techniques (or combinations thereof). In such embodiments, the prediction engines still generate similar outputs in response to similar inputs (as compared with their machine-learning neural network counterparts) without departing from the spirit of the present invention. Linn paragraph 160 disclosing in one embodiment, Action and Goals Optimization Engine 350c employs the scores 325a generated by Scoring Engine 350a (among other inputs) to generate actions 322c that facilitate these “win-win” outcomes between homeowners and various providers. For example, the integration of Home Efficiency scores enables “energy utility” providers to better manage energy usage by homeowners (e.g., by offering discounts for lower Home Efficiency scores over time). Similarly, Maintenance scores enable Warranty providers to incentivize more proactive preventive maintenance behavior over time. And Home Reliability scores enable Insurance providers to make much more targeted risk assessments, such as discounts for higher Home Reliability scores over time. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
and cause to be displayed, to the homeowner of the residential property via the graphical user interface, the one or more recommendations determined to be able to improve the home health score for the residential property. (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
Referring to claim 10,
Linn further discloses wherein the one or more processors are further programmed to: receive an alert from the home controller; (Linn paragraph 107 disclosing moreover, to the extent that functional modules within VFM Server 110 (or those included in Premises Controller 158 or in provider or other external servers) are implemented in software, such software is embodied in physical non-transitory computer-accessible storage media (i.e., memory) from which it is invoked for execution by one or more CPUs or other physical processing units. Linn paragraph 124 disclosing finally, in step 260, the recommend action is coordinated, communicated to and implemented by the relevant parties, such as providers, users and automated functionality built into Premises Controller 158. At that point, Home Health Record 126 is updated in step 290, and the process returns to step 201 to process raw sensor data during the next iteration of process 200. In one embodiment, the results of such implementation are incorporated into the update of Home Health Record 126
and cause to be displayed, to the homeowner of the residential property via the graphical user interface, the alert. (Linn paragraph 8 disclosing it should be noted that the term “maintenance” is used herein to address an array of different types of problems that occur with respect to a property's equipment and other infrastructure. For example, particular equipment may be in need of “repair” because one or more of its components are broken and need to be fixed. Or “preemptive maintenance” may be recommended to avoid a future repair, thereby enhancing reliability and reducing overall costs. Or the “operational performance” of particular equipment may fail to satisfy a homeowner's desired level of efficiency or comfort, or may simply be suboptimal, potentially indicating an emerging risk. Linn paragraph 40 disclosing in one embodiment, the VFM system also takes into account “Provider Goals” and capabilities. For example, the VFM system may recommend a service provider with specific expertise matching the suspected problem diagnosed via a series of related alert-action pairs. Beyond service providers, the VFM system also assesses the goals as well as the capabilities of other providers (e.g., insurers and home warranty companies) to align such goals with those of particular homeowners (as discussed below). Linn paragraphs 77-78 disclosing the Homecare Network of VFM system 100 also integrates other types of providers beyond service providers, and aligns their goals with those of particular homeowners. For example, networks of manufacturers, retailers and installers are integrated in a manner that enables the need for new equipment to be interpreted (with the homeowner's permission) as an opportunity to market, sell and install a particular model of equipment. Such need may be evident, for example, from a recommended alert-action pair, or simply by virtue of the age of the equipment or its performance characteristics over time, alone or in conjunction with a larger system within the home. Homeowners may reach out directly to such integrated providers via the Homecare Network. And the providers may find that their marketing efforts are far more targeted, and thus more effective, in light of their detailed knowledge of the home infrastructure and equipment performance. Linn paragraph 100 disclosing in this manner, homeowners receive alerts and corresponding actions, as well as provide feedback (e.g., requested information) or initiate queries. For example, a homeowner can submit a voice query regarding the relative efficiency of their AC system within a specified timeframe, and receive a summary of historic performance levels, as well as targeted comparative recommendations for upgrades or replacements that will be more efficient from the perspective of their AC system, as well as any individual item of equipment. Linn paragraph 124 disclosing finally, in step 260, the recommend action is coordinated, communicated to and implemented by the relevant parties, such as providers, users and automated functionality built into Premises Controller 158. At that point, Home Health Record 126 is updated in step 290, and the process returns to step 201 to process raw sensor data during the next iteration of process 200. In one embodiment, the results of such implementation are incorporated into the update of Home Health Record 126. Linn paragraph 163 disclosing other actions 322c may relate less to troubleshooting and fixing a discrete problem and more to ongoing preventive maintenance tasks. Here too, certain maintenance tasks may involve configuration procedures that are performed automatically by VFM Server 110, while others (e.g., changing a filter) may be performed by homeowners, and still others may require a service provider. Linn paragraphs 165-166 disclosing still other actions 322c may involve recommendations to purchase a device or item of equipment or component (to supplement, replace and/or upgrade a unit or component thereof) to address a systemic issue. For example, certain components may wear out with relative frequency, and proactive replacement of such components may result in extending the life of a piece of equipment, or even an entire system (e.g., due to the interdependencies among the operation of particular items of equipment). Here too, these recommendations are affected by the User Goals, whether at the level of an individual item of equipment or a system of equipment, or across the homeowner's entire residence. As was the case with Alert Generation Engine 350b, Action and Goals Optimization Engine 350c generates not only actions 322c, but also corresponding action attributes 324c. For example, the action attributes 324c identify the particular item(s) of equipment or components to which the action is targeted, as well as quantified details (e.g., a desired setting of an item of equipment, such as a thermostat). They also indicate a severity level, giving the homeowner and/or service provider a sense of the degree of urgency in performing that particular troubleshooting step (and in one embodiment the timeframe or condition necessary before the next step can be determined).)
Referring to claim 11,
Linn further discloses wherein the one or more processors are further programmed to transmit the alert to a third-party server. (Linn paragraph 107 disclosing moreover, to the extent that functional modules within VFM Server 110 (or those included in Premises Controller 158 or in provider or other external servers) are implemented in software, such software is embodied in physical non-transitory computer-accessible storage media (i.e., memory) from which it is invoked for execution by one or more CPUs or other physical processing units. Linn paragraph 122 disclosing in step 250, Prediction Engine Manager 117 processes the current alert-action pair (e.g., to implement that next recommended troubleshooting step) which, in one embodiment, involves coordination with Communications Assistant 124 and Premises and User Manager 114 (among other modules of VFM Server 110) to determine, in step 252, the interaction with and communication to the relevant premises 150 and associated users 160. For example, a recommended action may be determined to require communication to a homeowner to implement that action. In another embodiment, such action may be performed automatically by Premises Controller 158. Linn paragraph 123 disclosing similarly, in step 254, Prediction Engine Manager 117 coordinates with Communications Assistant 124 and Service Provider Manager 112 and Other Providers Manager 113 (among other modules of VFM Server 110) to determine the interaction and communication with any relevant Service Provider 130 or Other Provider 140. For example, a recommended action may be performed entirely or in part by a provider (e.g., scheduling a service call). In another embodiment, a relevant provider may supplement the recommended action with additional feedback. Linn paragraph 171 disclosing in some cases, he Communications Assistant 124 will also notify a Service Provider 130 or Other Provider 140. For example, a Service Provider 130 may elect to be notified only of alerts and/or actions (with respect to a specified group of homeowners 160) exceeding a particular severity level. But Service Providers 130 may well be notified even when an alert-action pair is not yet recommending actions requiring a service call.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Linn in view of Silverstein et al. (US 20210067366).
Referring to claims 7 and 18,
Linn does not teach or suggest wherein the one or more processors are further programmed to: receive, from the user device of the homeowner of the residential property, an instruction for a first smart device of the one or more smart devices of the residential property; and transmit the instruction to the home controller, wherein the home controller is configured to transmit the instruction to the first smart device, wherein the first smart device executes the instruction.
However Silverstein, which is directed to testing smart device functions within a smart environment teaches, wherein the one or more processors are further programmed to: receive, from the user device of the homeowner of the residential property, an instruction for a first smart device of the one or more smart devices of the residential property; (Silverstein paragraph 55 teaching Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. Silverstein paragraphs 64-65 teaching the network 112 may be any suitable communication network or combination of networks, such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet). The smart device controllers 116 may be configured to implement functions automatically and/or based on user input (e.g., voice commands). The smart device controllers 116 may include components of the computing device 12 of FIG. 1 and may be in the form of special computing devices (e.g., intelligent systems) configured to control automated functions of devices in a smart environment. For example, a smart device controller 116 may be configured to initiate functions of one or more smart devices 122 in a smart home or smart business environment. The term smart environment as used herein refers to a location (e.g., a home or building) including one or more smart devices 122 (e.g., IOT devices) controlled by one or more smart device controllers 116 via a network (e.g., local wireless network), wherein functions of the one or more devices may be controlled by the smart device controller 116 based on rules and/or manual input of users. Examples of smart devices (e.g., TOT devices) 122 that may be utilized in a smart environment include lighting or lighting controls 122a, smart appliances 122b, smart electrical outlets or electrical outlet controls 122c, and temperature control devices 122d (e.g., smart thermostats). In implementations, one or more smart device controllers 116 may be associated with a particular location. For example, smart device controllers 116 of the present invention may include a smartphone and personal computer each configured to remotely control lighting, heating, and/or electrical devices within a smart environment. Moreover, a smart device controller 116 may be part of a smart device itself or part of a smart device system, such as a controller for a security system, energy management system, lighting control system, temperature control system, or the like. In implementations, smart device controllers 116 include a communications module 117 configured to communicate with the test server 114. In embodiments, one or more test cases from the test server 114 are received by a communications module 117 of a smart device controller 116 during the test server's execution of the one or more test cases.)
and transmit the instruction to the home controller, wherein the home controller is configured to transmit the instruction to the first smart device, wherein the first smart device executes the instruction. (Silverstein paragraph 55 teaching Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20. Silverstein paragraphs 64-65 teaching the network 112 may be any suitable communication network or combination of networks, such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet). The smart device controllers 116 may be configured to implement functions automatically and/or based on user input (e.g., voice commands). The smart device controllers 116 may include components of the computing device 12 of FIG. 1 and may be in the form of special computing devices (e.g., intelligent systems) configured to control automated functions of devices in a smart environment. For example, a smart device controller 116 may be configured to initiate functions of one or more smart devices 122 in a smart home or smart business environment. The term smart environment as used herein refers to a location (e.g., a home or building) including one or more smart devices 122 (e.g., IOT devices) controlled by one or more smart device controllers 116 via a network (e.g., local wireless network), wherein functions of the one or more devices may be controlled by the smart device controller 116 based on rules and/or manual input of users. Examples of smart devices (e.g., TOT devices) 122 that may be utilized in a smart environment include lighting or lighting controls 122a, smart appliances 122b, smart electrical outlets or electrical outlet controls 122c, and temperature control devices 122d (e.g., smart thermostats). In implementations, one or more smart device controllers 116 may be associated with a particular location. For example, smart device controllers 116 of the present invention may include a smartphone and personal computer each configured to remotely control lighting, heating, and/or electrical devices within a smart environment.)
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 provision of recommendations for improving a home as taught in Linn to incorporate wherein the one or more processors are further programmed to: receive, from the user device of the homeowner of the residential property, an instruction for a first smart device of the one or more smart devices of the residential property; and transmit the instruction to the home controller, wherein the home controller is configured to transmit the instruction to the first smart device, wherein the first smart device executes the instruction as taught in Silverstein with the motivation of facilitating the control of home devices and/or settings. (Silverstein paragraph 64-65)
Response to Arguments
Applicant's arguments filed December 22, 2025 have been fully considered.
Applicant argues the claims are similar to those in Desjdardins where the claims were determined to be directed to artificial intelligence technology (including improving performance of a machine learning model). According to Applicant applying the principles from Desjardins the Specification provides several problems and benefits including 1) homeowners unaware of risks, corrections actions to take, or resources available to them, for example electrical issues and a lack of trusted remediation resources 2) detecting risks for home health and provide suggestions of available resources, and corrective actions 3) a single centralized app convenient for homeowners. See paragraphs 3-5 of the Specification. According to Applicant the technical solutions are provided from the Specification (See paragraphs 49, 58, 85, and 89) and are reflected in the claim limitations of:
(i) "convert the first element of external data to a format compatible for input into the smart home analysis machine learning model;" (ii) "output, from the smart home analysis machine learning model, a home health score for the residential property based at least in part upon one or more of the first element of home data and the converted first element of external data, the home health score representing a measure of health of the residential property;" (iii) "select, based upon the home health score, one or more candidate products and services listed in a digital marketplace associated with the connected home ecosystem, the one or more candidate products and services determined to be able to improve the home health score;" and/or (iv) "cause to be displayed, to a homeowner of the residential property via a graphical user interface of a user device of the homeowner, information about the one or more selected candidate products and services."
Applicant contends these recitations reflect improvements in machine learning models and/or the overall technology field of connected homes. According to Applicant the first element of external data is converted to a format compatible with the model because external data received from the one or more external sources may vary in format, where this conversion ensures that data input into the model will be processed correctly and efficiently in connection with determining the home health score, thereby also improving the efficiency in determining the home health score and the accuracy of the home health score, where the accuracy of the home health score is important because the home health score is a particularized score that is based upon the specific operating aspects of the system, within each individual home, and is used to match with particularized products services in the associated digital marketplace that are most relevant to the products/services that the home could implement to improve the home health score.
The Examiner respectfully disagrees viewing the additional elements identified are mere instructions to apply the abstract idea using generic computing components. The Examiner does not view the benefits to be an improvement to technology but rather an improvement to the abstract idea through the provision of recommendations pertaining to products that could assist a homeowner with unknown issues.
Applicant argues that the claims are not directed to an abstract idea, similar to the above argument in that the claims provide an improvement to technology in particular home monitoring and analysis technology in performing the detecting of risk and recommending protections or resources for users to review. According to Applicant the claims recite a technical solution to this problem by leveraging components of a connected home ecosystem to detect risks to home health and provide suggestions of various protections and/or remediation resources for such risk in a manner that is convenient for a homeowner, including recommendations of relevant product service listings in a digital marketplace based upon the individual characteristics of the home, and therefore enable the computer system to analyze elements of home and external data relating to the home to output individualized recommendations of products and/or services based upon the characteristics of the home by a leveraging a home controller and outputs from a machine learning model, solving a technical problem in conventional home monitoring system.
The Examiner respectfully disagrees viewing the claims as amended still recite the abstract idea and the additional elements as claimed are mere instructions to apply the abstract idea using generic computing components. Further Applicant’s proffered benefit the examiner views is an improvement to the abstract idea. The Examiner does not view the claims provide for an improvement to a connected home ecosystem as claimed but rather uses the connected home ecosystem for facilitating the performance of the abstract idea.
Applicant argues to specific practical and technical solution and improvement to connected (smart) home systems by selecting, based upon the particular home health score of a home as derived from the connected (e.g., smart) home systems by selecting, based upon the particular home health score of a home as derived from the connected (smart) home components and outputs from machine learning models, the most relevant products/services determined to be able to improve the home health score, that in no way preempts the entire alleged judicial exception.
The Examiner respectfully disagrees maintaining that any improvement is to the underlying abstract idea and not an improvement to technology or any of the other considerations enumerated MPEP 2106.04 (d). Regarding preemption the Examiner cites 2106.04:
The Supreme Court has explained that the judicial exceptions reflect the Court’s view that abstract ideas, laws of nature, and natural phenomena are “the basic tools of scientific and technological work”, and are thus excluded from patentability because “monopolization of those tools through the grant of a patent might tend to impede innovation more than it would tend to promote it.” Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (quoting Myriad, 569 U.S. at 589, 106 USPQ2d at 1978 and Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012)). The Supreme Court’s concern that drives this “exclusionary principle” is pre-emption. Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980. The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow (e.g., a particular mathematical formula such as the Arrhenius equation). See, e.g., Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69, 1971 (claims directed to “narrow laws that may have limited applications” held ineligible); Flook, 437 U.S. at 589-90, 198 USPQ at 197 (claims that did not “wholly preempt the mathematical formula” held ineligible). This is because such a patent would “in practical effect [] be a patent on the [abstract idea, law of nature or natural phenomenon] itself.” Benson, 409 U.S. at 71- 72, 175 USPQ at 676. The concern over preemption was expressed as early as 1852. See Le Roy v. Tatham, 55 U.S. (14 How.) 156, 175 (1852) (“A principle, in the abstract, is a fundamental truth; an original cause; a motive; these cannot be patented, as no one can claim in either of them an exclusive right.”).
While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. Rapid Litig. Mgmt. v. CellzDirect, Inc., 827 F.3d 1042, 1052, 119 USPQ2d 1370, 1376 (Fed. Cir. 2016). Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo (the Alice/Mayo test referred to by the Office as Steps 2A and 2B). Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1150, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016); Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379, 115 USPQ2d 1152, 1158 (Fed. Cir. 2015). It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible. Diamond v. Diehr, 450 U.S. 175, 191-92 n.14, 209 USPQ 1, 10-11 n.14 (1981) (“We rejected in Flook the argument that because all possible uses of the mathematical formula were not pre-empted, the claim should be eligible for patent protection”).
Applicant argues the claims recite more than well-understood routine conventional activities with respect to leveraging a machine learning model to output a home health score of a home based upon elements of home and external data and in connection with a home controller, and while referencing the home health score against products/services in a digital marketplace associated with the connected home, so that only the most relevant products/services are recommended with respect to improving the home health score. According to Applicant the claims include a combination of limitations that operate in a non-conventional and non-generic way to determine the overall health of a home via connected (e.g. smart) home components such as a home controller and using the home health score as a basis for selecting products/services determined to be able to improve the home health score (making the home more safe, efficient, etc.) where this is further evidenced by the prior art rejections being overcome.
The Examiner respectfully disagrees because the limitations that Applicant contends are non-conventional and non-generic are steps of the abstract idea. Further the additional elements identified are merely applied for facilitating the performance of the abstract idea and do not add significantly more. Regarding the supposed lack of prior art the Examiner cites MPEP 2106.05:
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) (“The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.”). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103) is further discussed in MPEP § 2106.05(d).
Therefore, for the foregoing reasons the Examiner has maintained the 101 rejection.
Applicant’s amendments and arguments, on pages 17—20 of the Remarks, regarding the prior art rejections the Examiner finds unpersuasive. Applicant argues that the prior art of record fails to teach the claims as amended. The Examiner respectfully disagrees, viewing Aplciant’s arguments are rendered moot in view of the newly cited portions of Linn in response to Applicant’s amendments. Therefore, the Examiner has maintained the prior art rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sahu et al. (US 20160112394) -directed to a personalized provider recommendation engine.
Rydin (US 20230153869) -directed to recommending products and services.
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