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 Objections
Claims 1-20 are objected to because of the following informalities: Referring to claims 1, 8, and 15, the claims recite “receiving, by one or more processors, a user proposal to improve a home score factors of one or more home score factors”. Please amend “a home score factors” to read “at least one home score factor”. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 recite a method (process), Claims 8-14 recite a computing device (machine) and Claims 15-20 recite a tangible, non-transitory computer-readable medium (manufacture) and therefore fall into a statutory category. The Examiner is interpreting the computing device and tangible, non-transitory computer-readable medium perform the steps of the method for examination purposes.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
The claims as a whole recites a method, a computing device and tangible, non-transitory computer-readable medium for a manner of creating an updated user proposal based on the analysis of collected information, which under its broadest reasonable interpretation, covers concepts for Certain Methods of Organizing Human Activity and covers concepts capable of being performed in Mental Processes.
The abstract idea portion of the claims is as follows:
(Claim 1) A [computer-implemented] method for evaluating score and generating home construction recommendations for a property, the computer-implemented method comprising:
(Claim 8) [A computing device for] evaluating score and generating home construction recommendations for a property, [the computing device comprising: one or more processors; a communication unit; and a non-transitory computer-readable medium coupled to the one or more processors and the communication unit and storing instructions thereon that, when executed by the one or more processors, cause the computing device to:]
(Claim 15) [A tangible, non-transitory computer-readable medium storing instructions for] evaluating score and generating home construction recommendations for a property that, [when executed by one or more processors of a computing device, cause the computing device to]:
retrieving, [by one or more processors], home data for a property including sensor data captured [by one or more sensors associated with the property], the sensor data including identification data for the one or more sensors;
receiving, [by the one or more processors], a user proposal to improve a home score factors of one or more home score factors;
determining, [by the one or more processors and] based upon the home data for the property, one or more updated home score factors, wherein the determining includes:
analyzing, [using a trained machine learning data evaluation model], the home data for the property to determine home characteristic data for the property,
analyzing, [using the trained machine learning data evaluation model], the home characteristic data for the property and the user proposal to determine predicted home characteristic data for the property,
weighting, [using the trained machine learning data evaluation model], the predicted home characteristic data using at least the identification data to generate weighted home characteristic data,
and determining, based upon the weighted home characteristic data for the property, the one or more updated home score factors;
and generating, [by the one or more processors], an updated proposal based on the weighted home characteristic data and the user proposal.
Here the claims are directed to managing personal behavior (following rules or instructions) but for the recitation of generic computer components. Additionally, the claims are directed to concepts capable of being performed in the human mind (including an observation, evaluation, judgment, opinion). In the present application the claims recite concepts covering a manner of creating an updated user proposal based on the analysis of collected information. (See paragraphs 3 and 5).
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 MPEP 2106.04. Additionally, if a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in human mind it falls under the Mental Processes 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:
Computer-implemented. (See paragraphs 42 and 44 of the Specification)
A computing device. (See paragraph 44 and Figure 1 el. 117 of the Specification.)
One or more processors. (See paragraph 47 of the Specification)
A communication unit. (See paragraphs 100 and 103 of the Specification)
A non-transitory computer-readable medium storing instructions. (See paragraph 137 of the Specification)
A tangible, non-transitory computer-readable medium. (See paragraphs 137 and 139 of the Specification)
One of sensors. (See paragraph 45 of the Specification)
Instructions. (See paragraph 54 of the Specification)
A trained machine learning data evaluation model. (See paragraphs 79 and 84 of the Specification)
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)) 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.
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 2-6, 7, 11, 13, and 18 further define the abstract idea as identified and do not integrate the abstract idea into a practical or add significantly more. Therefore 2-6, 7, 11, 13, and 18 are considered to be patent ineligible.
Dependent claim 7 further defines the abstract idea as identified. Additionally, the claim recites the additional elements of generic one or more sensors (See paragraph 45) and trained machine learning evaluation model (See paragraphs 79 and 84) 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 add significantly more. Therefore claim 7 is considered to be patent ineligible.
Dependent claims 9-10 and 12 further define the abstract idea as identified. Additionally, the claim recites the additional elements of generic non-transitory computer-readable medium (See paragraph 137), instructions (See paragraph 54), one or more processors (See paragraph 47), and computing device (See paragraph 44 and Figure 1 el. 117) 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 add significantly more. Therefore claims 9-10 and 12 are considered to be patent ineligible.
Dependent claim 14 further defines the abstract idea as identified. Additionally, the claim recites the additional elements of generic non-transitory computer-readable medium (See paragraph 137), instructions (See paragraph 54), one or more processors (See paragraph 47), and computing device (See paragraph 44 and Figure 1 el. 117), one or more sensors (See paragraph 45), and trained machine learning evaluation model (See paragraphs 79 and 84) 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 add significantly more. Therefore claim 14 is considered to be patent ineligible.
Dependent claims 16-17 and 19 further define the abstract idea as identified. Additionally, the claim recites the additional elements of generic tangible non-transitory computer-readable medium (See paragraphs 137 and 139), instructions (See paragraph 54), one or more processors (See paragraph 47), and computing device (See paragraph 44 and Figure 1 el. 117), one or more sensors (See paragraph 45), and trained machine learning evaluation model (See paragraphs 79 and 84) 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 add significantly more. Therefore claims 16-17 and 19 are considered to be patent ineligible.
Dependent claim 20 further defines the abstract idea as identified. Additionally, the claim recites the additional elements of generic tangible non-transitory computer-readable medium (See paragraphs 137 and 139 of the Specification), instructions (See paragraph 54 of the Specification), one or more processors (See paragraph 47), and computing device (See paragraph 44 and Figure 1 el. 117), one or more sensors (See paragraph 45), and trained machine learning evaluation model (See paragraphs 79 and 84) 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 add significantly more. Therefore claim 20 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.
No Prior Art Applied
The prior art of record fails to at least explicitly disclose or teach in the independent claims: referencing claim 1 as exemplary: “weighting, using the trained machine learning data evaluation model, the predicted home characteristic data using at least the identification data to generate weighted home characteristic data, and determining, based upon the weighted home characteristic data for the property, the one or more updated home score factors; and generating, by the one or more processors, an updated proposal based on the weighted home characteristic data and the user proposal” as claimed by Applicant.
The closest pieces of identified prior art are the following:
Bentley et al. (US 20190251520), which is directed to monitoring, maintaining, and upgrading a property, discusses collecting sensor data pertaining to an item as sensed by a sensor and using property traits that are weighed computing a home fitness index score, where the weights can be based on feedback from an AI algorithm. (Bentley paragraphs 7, 99-100, and 109). Bentley further discusses using AI and predictive analytics for assessing issues pertaining to items to a house based on comparison using defined thresholds (Bentley paragraph 142). Nimry et al. (US 20220138683), which is directed to property inventory tracking, discusses monitoring an operation status of individual items at a property for calculating health scores for the items which can be weighed based on the relative importance and weighting used in calculating, and provide the health scores for individual items to a user for review. (Nimry paragraphs 5, 60, 69, and 84).
Hayward (US 20210151195), which is directed to which is directed to determining a health index of a property area, discusses determining weighting and prevalence values for property attributes using a machine learning algorithm for generating a property index score based on the monitoring of sensors on a property. (Hayward paragraphs 97-102 and 359). Szott (US 20190304025), which is directed to generating an assessment of safety parameters using sensors and sensor data, discusses using a machine learning model for generating an assessment of a safety parameter pertaining to an area and discusses how each of the safety parameters may be provided to a user for review. (Szott paragraphs 35 and 57).
Huber et al. (US 20230145448), which is directed to predicting building faults, discusses using active node table for equipment discovery or can provide an equipment model defining equipment attributes, and trends as well. (Huber paragraphs 89-90). Huber further teaches a data collector can tag received datapoints with the timestamp and the device identifier. (Huber paragraphs 104-106). Huber also discusses inputting measurements of various portions of pieces of building equipment in a ML model in which a fault is likely to occur in a piece of building equipment and a root cause of such faults and provide maintenance recommendations to a user. (Huber paragraphs 99, 110-111 and 118). Huber further teaches the fault prediction system trains the prediction models of equipment models via the fault prediction manager that feeds labeled training data for particular pieces of equipment for use in predicting faults. (Huber paragraphs 120-121).
Conway (US Patent No. 11,003,334), which is directed to home services condition monitoring, discuses collecting sensor data using various sensors for recommending services or products for a home. (Conway column 8 lines 4-37 and column 11 line 19 to column 12 line 47).
Aspro et al. (US 20220028567), which is directed to using sensor data for home monitoring and control, discusses obtaining sensor readings from various sensors associated with a house and compute a home score based on the obtained sensor readings and provide and rank maintenance recommendations for a property using machine learning models. (Aspro paragraphs 59-60, 65, 78, and 84-85).
Venkatesh et al. (US 20210199327), which is directed to predictive presence scheduling for a thermostat using machine learning, teaches collecting event data from one or more devices to generate machine learning model for predicting an occupancy schedule and/or to set a point temperature for a space, this event data may comprise various types of information include a device identifier. (Venkatesh paragraph 29). Venkatesh continues that the ML model is generated uses the data obtained from an occupancy history log for adjusting weights and other parameters of the model. (Venkatesh paragraphs 52-53 and 55).
Papadopoulos (US 20210109485), which is directed to applying semantic information to data in a building management system, teaches a live data handler can communicate information acquired from the sensors of the system including the identifier of the building device and the historical data handler generates training datasets to apply to the ML models including information associated with a particular identifier. (Papadopoulos paragraphs 96 and 100). Papadopoulos further teaches the historical database can for example acquire sensor data from sensors of a supply fan the values can be associated with device identifiers and the time series data inputted into the ML models include the device identifier. (Papadopoulos paragraphs 102 and 114-117).
Hayward et al. (US Patent No. 10,497,250), which is directed to detecting damage and/or other conditions associated with real property, teaches storing dynamic characteristic data indicative of dynamic, physical characteristics detected by a plurality of sensors. (Hayward column 2 line 60 to column 3 line 18). Hayward further teaches training a predictive model for use in predicting one or more conditions associated with a building including particular damage for providing to a user. (Hayward column 3 lines 22-41). Hayward further teaches how inputs to a ML model may include historical claim information such as make, model, year of appliances, and home telematics data received from a smart home controller such as how long a stove is on. (Hayward column 8 lines 6-19). Hayward further teaches how the telematics data collected may include sensor data and use this information for taking a subsequent action in response to the analysis of the collected data. (Hayward column 11 line 58 to column 12 line 20). Hayward further teaches a damage detector may detect damage and/or other conditions to a building using a trained model. (Hayward column 21 lines 6-31). Hayward further teaches transmitting indications of discovered conditions and retraining the model using subsequent acquired data. (Hayward column 25 line 57 to column 26 line 28).
Carone (US Patent No. 11,055,797), which is directed to autonomous property monitoring, teaches a status may refer to a physical condition associated with a designated object or region within the property. (Carone column 3 lines 34- 54). Carone further teaches performing analysis of acquired sensor and image data. (Carone column 7 lines 32-47). Carone further teaches objects for example, cabinet and stove, may be assigned to a category for example kitchen, the system computes the status score for each of the objects and associated categories. (Carone column 19 line 56 to column 20 line 25)
Yager et al. (US Patent No. 11,783,423), which is directed to monitoring and/or sensing of one or more home devices from one or more homes, teaches acquiring sensor data pertaining to operating characteristics of one or more home devices and second data associated with user and user’s behavior in the home. (Yager column 3 lines 6—38). Yager teaches the invention determines whether the operation of devices is within an acceptable range, identify any potential issues or failures, identify or adjust insurance rates based on the received data, provide insurance incentives for improvement based on the data, etc. (Yager column 5 lines 5-26).
Mowatt et al. (US 20180211339), which is directed to generating property and tenant insights based on sensor devices such as IOT devices, teaches individual score may be determined for each factor, and the individual score may be assigned a weight to generate a score for each room (Mowatt paragraph 39).
Carey (US 20200134752) – directed to a house hub. Carey teaches how users can provide information related to items at a property for example water heater, smoke detector, roof, electrical system among others and may acquire sensor data related for the item for assessing whether to generate an alert notification for a user. (Carey paragraphs 88-89). Carey explains how the sensor data can be categorized and compared with a threshold for generating the notifications that a user may select to initiate a maintenance or service request and can provide the user a list of service providers to complete the request. (Carey paragraphs 90-91). Carey explains that the user may receive a notification describing a request for maintenance being received by the server which responds with a list of eligible service provides. (Carey paragraphs 112-113)
Schreier et al. (US 20170365008) – directed to parsing databases to generate customized recommendations for a home. Schreier teaches providing recommendations to users regarding home assessment based on identifying publicly available information and customer-specific information to provide recommendations regarding facts, potential risks, and tips for the user. (Schreier paragraph 16). Schreier explains the recommendations can pertain to maintenance projects for users. (Schreier paragraph 62). Schreier teaches that the recommendation system identifies customer information that relates to features of the user’s home and types of materials used to provide tips for the home for example, maintenance, renovation, or part replacement. (Schreier paragraphs 66-67).
Chen et al. (US 20210390647) -directed to automated staging and capture of real estate negotiations. Chen explains that the client devices receive and transmit proposals and counter-proposal related to a negotiation and the client device may receive outside data and information related to the asset such as location, maintenance history, etc. to train at least one machine learning algorithm to provide intelligent deal-term suggestions based on the trained machine learning algorithm. (Chen paragraphs 25 and 65).
Waslander et al. (US 20190108603) – directed to property enhancement services. Waslander teaches the Home Visit App may allow a user to provide information regarding a home renovation scope, this information can be obtained by guiding a user through various operations and questions to generate a scope of work and associated cost estimates. (Waslander paragraph 489). Waslander explains that the results of the questionnaire and floor plan annotation may be used to generate a scope of work document and cost estimate, a set of broad style categories that may determine what appliances, finishings, and functional components may be recommended, the results of the questionnaire and floor plan annotation may be used to create a 3D rendering of the potential end state of the home after renovation. Further an interface where the user and homeowner can make adjustments to the initial cost estimate by modifying the scope may be provided and questions that may be asked in the application to create scope and cost estimates may vary by room type, functional features, and finishes among others. (Waslander paragraphs 501-505). Waslander teaches that machine learning uses project data and property data to predict, estimate, and/or generate a likelihood unforeseen site condition. (Waslander paragraph 520).
Noel et al. (US Patent No. 10,311,529) – directed to applying machine learning to create digital request for proposal. Noel teaches that machine learning algorithms are used to determine the cost estimate for a consumer’s desired projects and recommendations to help the user reach their goal, the system collects data from the user through the user interacting with the application such as via provided by or selected by the user such as a project scope. (Noel column 10 line 61 to column 11 line 11). Noel discusses the system uses data collected from the user including selections made by the user on the application or otherwise provided by the user to generate an RFP. (Noel column 13 lines 36-45). Noel explains that a user may select a project scope from a set of predefined project scope or allow the consumer to customize their package, and generate a project cost estimate based on the user selected or inputted information that relates to the property, for example square footage, location, preference for construction material among other, users may modify their inputs to update the estimated costs, and once they have finalized their selections the system can create a request for proposal based on the selected project scopes by the user. (Noel column 15 line 26 to column 16 line 57). Noel discusses how machine learning algorithm may assist a user in recommending design elements or can assist a user in generating a building proposal, for example determining a project scope based on variables such as location, budget, home area, property type and age, and past project goals. (Noel column 18 lines 12-45).
While the prior art may teach concepts covering, retrieving home data for a property including sensor data that further includes identification data for the sensors, receiving a user proposal, determining based upon the home data one or more updated home score factors, analyzing using a trained machine learning data evaluation model, the home data for the property to determine home characteristic data for the property and furthers considers using machine learning for providing intelligent suggestions for inclusion in a proposal, to determine cost estimates, or to predict a likelihood of lost condition the prior fails to teach or suggest “weighting, using the trained machine learning data evaluation model, the predicted home characteristic data using at least the identification data to generate weighted home characteristic data, and determining, based upon the weighted home characteristic data for the property, the one or more updated home score factors; and generating, by the one or more processors, an updated proposal based on the weighted home characteristic data and the user proposal” as claimed by Applicant.
Further the Examiner notes the following applications and associated patent number were considered for purposes of non-statutory double patenting:
17/816,379 (US Patent No. 12,315,024)
17/972,261 (US Patent No. 12,277,616)
The Examiner views that while both Patents cover some of the limitations of the present claims and Waslander teaches or suggests the concepts of receiving information from a user regarding a project scope, that can be modified by the user and that machine learning uses both project data and property data to predict a condition (Waslander paragraphs 489, 501-505,and 520), and Noel explains a user generating a proposal based on a user selected scope or a scope determined using machine learning and property information including past goals. (Noel column 13 lines 36-45, Noel column 13 lines 36-45, Noel column 15 line 26 to column 16 line 57, and Noel column 18 lines 12-45 ), the prior art and identified patents fails to teach or suggest at least referencing claim 1 as exemplary “generating, by the one or more processors, an updated proposal based on the weighted home characteristic data and the user proposal” and therefore determined that non-statutory double patenting would not be applicable. The Examiner views the prior art only provides for generating a proposal based on analyzed information rather than generating an updating user proposal based on the weighted home characteristics and user proposal as claimed by Applicant.
Therefore, there is no current art rejection or double patenting rejection applied for claims 1-20; however, the examiner notes the outstanding claim rejection under 35 USC § 101 for claims 1-20 and claim objections for claims 1-20. Therefore, the claims are not indicated as allowable at this time.
Conclusion
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 Monday- Friday 8:30 am - 5:30 pm.
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/Michael J. Monaghan/Examiner, Art Unit 3629
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