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 1-20 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 recite a method, a computing device and tangible, non-transitory computer-readable medium for 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 factor 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,
determining, based upon the weighted home characteristic data for the property, the one or more updated home score factors;
[training, by the one or more processors, the trained machine learning data evaluation model using at least the weighted home characteristics data and the one or more updated home score factors;] and
generating, [by the one or more processors], an updated proposal based upon the weighted home characteristic data and the user proposal.
Here the claims recite concepts coverings are directed to managing personal behavior (following rules or instructions) but for the recitation of generic computer components. Additionally, the claims recite 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))
Regarding “training, by the one or more processors, the trained machine learning data evaluation model using at least the weighted home characteristics data and the one or more updated home score factors” the Examiner views this limitation as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this limitation is viewed as equivalent to “apply it” for merely implementing the abstract idea. (See MPEP 2106.05 (f) and paragraphs 38 and 79 of the Specification).
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.
Response to Arguments
Applicant's arguments filed July 15, 2026 have been fully considered.
Applicant’s amendments and arguments, on page 8 of the Remarks, regarding the claim objections the Examiner finds Applicant’s amendments persuasive. Therefore, the Examiner has withdrawn the claim objections.
Applicant’s amendments and arguments, on pages 8-11 of the Remarks, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant notes the claims recite in pertinent part “training, by the one or more processors, the trained machine learning data evaluation model using at least the weighted home characteristics data and the one or more updated home score factors; and generating, by the one or more processors, an updated proposal based upon the weighted home characteristic data and the user proposal. Applicant argues the claims provide for utilizing unique training techniques that improve the training of ML models. According to Applicant, the system may generate communication including a representation of the home telematics data includes augmenting the communication with the identity data, where this augments the home data by weighting the predicted home characteristic data using the identification data that allows the system to better determine and/or modify weights of the model during training. Applicant contends this is reflected through:
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, determining, based upon the weighted home characteristic data for the property, the one or more updated home score factors; training, by the one or more processors, the trained machine learning data evaluation model using at least the weighted home characteristics data and the one or more updated home score factors;
According to Applicant, the amended claims improve the functionality of the ML evaluation model by at least retraining the model for future iterations using home data and/or communications augmented with identification data, where the ML evaluation model is particularly trained using the weighted home characteristic data (weighted based on the identification data) and the updated home score factors, that improve the model. Applicant continues the ML evaluation model may more accurately analyze home data to generate a more accurate proposal and provides for selectively training the ML evaluation model using data that is particularly weighted at least based on home data augmented with the identification data associate with sensors captured sensor data.
The Examiner respectfully disagrees, the Examiner views the claims to recite retrieving sensor data that includes identification information for the one or more sensors, rather than augmenting the sensor data to further include the identification information for the one or more sensors. The Examiner views the concepts pertaining to the training/retraining of the ML evaluation model amounts to results solution-oriented language that is equivalent to no more than mere instructions to apply the abstract idea using generic computing components that does not integrate the abstract idea into a practical application or adds significantly more. (MPEP 2106.05 (f)). MPEP 2106.05 (f) states:
Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356.
In the instant claims there is no restriction on how the training is accomplished or description of the mechanism for accomplishing the training/retraining, rather the training/retraining as claimed only specifies the type of data (weighted home characteristics data and the one or more updated home score factors) used in training the trained machine learning evaluation model. The Examiner does not view the utilization of the identification data to weight predicted home characteristic as claimed to be an additional element but as part of the identified recited abstract idea as identified in the Step 2A Prong 1 Analysis. The Examiner further views that the limitations referenced by Applicant are steps of the abstract idea that are merely applied using the trained machine learning evaluation model and training step. The Examiner views the alleged improvement to the ML evaluation model is not a technical improvement or other consideration enumerated under MPEP 2106.04 (d). Rather the manner of training the ML evaluation model and per se improvements in ML are rather directed to improving the ability in generating more accurate proposals, which is an improvement to the abstract idea.
Applicant argues that the claims are similar to those in Desjardins because augmenting the data used to train the ML evaluation model of claim 1 is akin to adjusting the parameters of the ML model as recited in Desjardins.
The Examiner respectfully disagrees reiterating they do not view the claims to provide for augmenting data as claimed but rather retrieves sensor data including identification data. Further the Examiner views that the training of the ML evaluation model amounts to mere instructions to apply the abstract idea using generic computing components as discussed prior. Additionally, the Examiner maintains the improvement is to the abstract idea rather than a technical improvement as illustrated in Desjardins.
Therefore, for the foregoing reasons the Examiner has maintained the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mason et al. (US Patent No. 12,602,600) – directed to training and applying predictive ML models for parcels of real property.
Simkoff et al. (US Patent No. 10,255,550) -directed to training and applying machine learning using multiple input types.
THIS ACTION IS MADE FINAL. 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 Monday- Friday 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J. Monaghan/Examiner, Art Unit 3629
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