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 .
DETAILED CORRESPONDENCE
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Status of Claims
Claims 33, 38, 40, 61, 63, 64 have been amended (NOTE: In claim 61, on Page 7, “do not include” has been struck-through. However, this amendment was previously done in the previous claim set received on 6/30/2025. Additionally, in claim 33 on Page 2, the letter “s” has not been underlined in the term “models” in limitation 5.).
Claims 1 – 32, 42, 45, 47 – 60, 65, 67 – 69 have been cancelled.
No claims have been added.
Claim Objections
Claim 64 is objected to because of the following informalities: the term “mode” should be “model” in limitation 5.
Appropriate corrections are required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 33 – 41, 43, 44, 46, 61 – 64, 66 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 33, 61, 64 recite the limitation "estimation facility" in the last line of the newly presented “storing” limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 33 – 41, 43, 44, 46, 61 – 64, 66 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In regards to claims 33, 61, 64, the Examiner that the following is new matter:
“estimation architecture”
“estimation facility”
There is no support in the specification that labels the plurality of sub-models and meta-model as an “estimation architecture” or “estimation facility”. Additionally, the claims recite that the “estimation architecture” comprises the sub-models and metal-model, thereby indicating that the “estimation architecture” may include additional elements, i.e. “comprises” is open-ended, which, again, the specification does not provide support for. That is to say, one of skill in the art would be unable to determine what it is that the applicant was in possession of with regards to what is included or not included in an “estimation architecture” because the specification does not define this concept.
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 33 – 41, 43, 44, 46, 61 – 64, 66 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
accessing attributes of the distinguished home;
accessing information identifying a set of geographic features that are near the distinguished home;
determining a distance between each of the set of geographic features and the distinguished home;
establishing an estimation architecture comprising sub-models including a heat map model, a feature-sensitive model, a feature-insensitive model and meta-model;
creating models within the estimation architecture, including:
creating the heat map model for a plurality of geographic features within the distinguished geographic area, including the set of geographic features, to estimate relative values of the plurality of geographic features within the distinguished geographic area;
creating the feature-insensitive model using a plurality of home attributes and the relative values of the plurality of geographic features estimated by the heatmap to output a feature-sensitive valuation;
creating the feature-insensitive model using the plurality of home attributes to output a feature-insensitive valuation;
creating the meta-model to estimate a value of a home based on the plurality of home attributes, the feature-sensitive valuation outputted by the feature-sensitive model and the feature-insensitive valuation outputted by the feature-insensitive model
storing parameter values generated during creating of the sub-models, the parameter values including data including how distances between homes and nearby geographic features influence valuation outputs of the estimation facility;
computing the estimated value for the distinguished home by applying a metal-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, wherein applying comprises:
computing estimated relative values for the set of geographic features associated with the distinguished home by applying the heat map model;
computing a feature-insensitive value of the distinguished home by applying the feature-insensitive model to the accessed attributes, wherein the feature-insensitive model [utilizes] data that indicates attributes of first homes that are independent distances between a first given home and first given geographic features near the first given home;
computing a feature-sensitive value of the distinguished home by applying the feature-sensitive model to the accessed attributes, the estimated relative values for the set of geographic features from the heat map model and the information identifying the set of geographic features that are near the distinguished home, wherein the feature-sensitive model [utilizes] data that indicates attributes of second homes that include distances between a second given home and second given geographic features near the second given home;
automatically generating a distance-dependent weighting function that is based on the parameter values and used to determine a first-weight and a second weight applied to the feature-sensitive value and the feature-insensitive value, respectively and
computing the estimated value for the distinguished home, via metal-model, by applying the distance-dependent weighting function and generating a weighted average of the feature-sensitive value and the feature-insensitive value, wherein the distance-dependent weighting function includes weighting the feature-sensitive value at the first-weight that is greater than that of the second-weight of the feature-insensitive value in response to at least one distance between the distinguished home and at least one of the set of geographic features satisfying a threshold distance and weighting the feature-sensitive value at the first-weight that is less than that of the second-weight of feature-insensitive value in response to the distance between the distinguished home at each of the set of geographic features not meeting the threshold distance
updating at least one of the first-weight or the second-weight by generating an error value using a sale price of the distinguished home and the estimated value of the distinguished home; and
generating an updated estimated value for the distinguished home by reapplying the meta-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, such that reapplying the meta-model comprises using the at least one updated first-weight or the second weight as determined by the distance-dependent weighting function.
The invention is directed towards the abstract idea of asset valuation, in this case, estimating the value of a home based on how it compares against other homes, which corresponds to both “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” as it is directed towards steps that can be performed in the human mind and/or through the aid of pen and paper, e.g., a human reviewing information of a particular home and comparing it against other homes and performing a mathematical calculation to estimate its value or a first human providing a second human information of a particular home and other homes so that either human can compared the homes with another and perform a mathematical calculation to estimate its value, as well as creating various models based on collected data to assist with determining the value of a real estate property, which is based on the collection and comparison of information and, based on a rule, identify options.
The limitations of:
accessing attributes of the distinguished home;
accessing information identifying a set of geographic features that are near the distinguished home;
determining a distance between each of the set of geographic features and the distinguished home;
establishing an estimation architecture comprising sub-models including a heat map model, a feature-sensitive model, a feature-insensitive model and meta-model;
creating models within the estimation architecture, including:
creating the heat map model for a plurality of geographic features within the distinguished geographic area, including the set of geographic features, to estimate relative values of the plurality of geographic features within the distinguished geographic area;
creating the feature-insensitive model using a plurality of home attributes and the relative values of the plurality of geographic features estimated by the heatmap to output a feature-sensitive valuation;
creating the feature-insensitive model using the plurality of home attributes to output a feature-insensitive valuation;
creating the meta-model to estimate a value of a home based on the plurality of home attributes, the feature-sensitive valuation outputted by the feature-sensitive model and the feature-insensitive valuation outputted by the feature-insensitive model
storing parameter values generated during creating of the sub-models, the parameter values including data including how distances between homes and nearby geographic features influence valuation outputs of the estimation facility;
computing the estimated value for the distinguished home by applying a metal-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, wherein applying comprises:
computing estimated relative values for the set of geographic features associated with the distinguished home by applying the heat map model;
computing a feature-insensitive value of the distinguished home by applying the feature-insensitive model to the accessed attributes, wherein the feature-insensitive model [utilizes] data that indicates attributes of first homes that are independent distances between a first given home and first given geographic features near the first given home;
computing a feature-sensitive value of the distinguished home by applying the feature-sensitive model to the accessed attributes, the estimated relative values for the set of geographic features from the heat map model and the information identifying the set of geographic features that are near the distinguished home, wherein the feature-sensitive model [utilizes] data that indicates attributes of second homes that include distances between a second given home and second given geographic features near the second given home;
automatically generating a distance-dependent weighting function that is based on the parameter values and used to determine a first-weight and a second weight applied to the feature-sensitive value and the feature-insensitive value, respectively and
computing the estimated value for the distinguished home, via metal-model, by applying the distance-dependent weighting function and generating a weighted average of the feature-sensitive value and the feature-insensitive value, wherein the distance-dependent weighting function includes weighting the feature-sensitive value at the first-weight that is greater than that of the second-weight of the feature-insensitive value in response to at least one distance between the distinguished home and at least one of the set of geographic features satisfying a threshold distance and weighting the feature-sensitive value at the first-weight that is less than that of the second-weight of feature-insensitive value in response to the distance between the distinguished home at each of the set of geographic features not meeting the threshold distance
updating at least one of the first-weight or the second-weight by generating an error value using a sale price of the distinguished home and the estimated value of the distinguished home; and
generating an updated estimated value for the distinguished home by reapplying the meta-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, such that reapplying the meta-model comprises using the at least one updated first-weight or the second weight as determined by the distance-dependent weighting function,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, as well as training and updating models. That is, other than reciting a generic processor executing computer code stored on a computer medium, as well as training and updating models nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium, as well as training and updating models, in the context of this claim encompasses a user(s) to compare homes and perform a mathematical calculation using models to estimate the value of a home based on the comparison. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, as well as training and updating models, then it falls within the “Mental Processes”, “Certain Methods of Organizing Human Activities”, and “Mathematical Concepts” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium, as well as training and updating models, to communicate and refer to stored information, as well as performing operations that a human can perform in their mind or using pen and paper, i.e. calculate an estimated value of a home based on comparing a home of interest with other homes. The generic processor executing computer code stored on a computer medium, as well as training and updating models, in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium can perform the insignificant extra solution steps of communicating and referring to stored information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium are merely being applied to perform the steps that can be performed in the human mind or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium.
Although the claims recite “heat map model”, “feature-sensitive model”, “feature-insensitive model” and “meta-model”, the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying the “heat map model”, “feature-sensitive model”, “feature-insensitive model” and “meta-model” are simply application of computer models, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being “heat map model”, “feature-sensitive model”, “feature-insensitive model” and “meta-model” are a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards real estate property evaluation and the data associated with real estate properties that can affect a property’s value so that a mathematical calculation can be performed to estimate the value of a particular real estate property based on a comparison against other real estate properties. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. The Examiner asserts that the claimed invention fails to recite any iterative process being performed on the machine learning algorithm/model in order to demonstrate that the machine learning algorithm/model is being improved upon, i.e. a demonstration that would support an improvement upon machine learning technology. Referring to MPEP § 2106.05(f), the training and re-training (updating) are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning models. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, 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. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor executing computer code stored on a computer medium as well as training and updating models, to perform the steps of:
accessing attributes of the distinguished home;
accessing information identifying a set of geographic features that are near the distinguished home;
determining a distance between each of the set of geographic features and the distinguished home;
establishing an estimation architecture comprising sub-models including a heat map model, a feature-sensitive model, a feature-insensitive model and meta-model;
creating models within the estimation architecture, including:
creating the heat map model for a plurality of geographic features within the distinguished geographic area, including the set of geographic features, to estimate relative values of the plurality of geographic features within the distinguished geographic area;
creating the feature-insensitive model using a plurality of home attributes and the relative values of the plurality of geographic features estimated by the heatmap to output a feature-sensitive valuation;
creating the feature-insensitive model using the plurality of home attributes to output a feature-insensitive valuation;
creating the meta-model to estimate a value of a home based on the plurality of home attributes, the feature-sensitive valuation outputted by the feature-sensitive model and the feature-insensitive valuation outputted by the feature-insensitive model
storing parameter values generated during creating of the sub-models, the parameter values including data including how distances between homes and nearby geographic features influence valuation outputs of the estimation facility;
computing the estimated value for the distinguished home by applying a metal-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, wherein applying comprises:
computing estimated relative values for the set of geographic features associated with the distinguished home by applying the heat map model;
computing a feature-insensitive value of the distinguished home by applying the feature-insensitive model to the accessed attributes, wherein the feature-insensitive model [utilizes] data that indicates attributes of first homes that are independent distances between a first given home and first given geographic features near the first given home;
computing a feature-sensitive value of the distinguished home by applying the feature-sensitive model to the accessed attributes, the estimated relative values for the set of geographic features from the heat map model and the information identifying the set of geographic features that are near the distinguished home, wherein the feature-sensitive model [utilizes] data that indicates attributes of second homes that include distances between a second given home and second given geographic features near the second given home;
automatically generating a distance-dependent weighting function that is based on the parameter values and used to determine a first-weight and a second weight applied to the feature-sensitive value and the feature-insensitive value, respectively and
computing the estimated value for the distinguished home, via metal-model, by applying the distance-dependent weighting function and generating a weighted average of the feature-sensitive value and the feature-insensitive value, wherein the distance-dependent weighting function includes weighting the feature-sensitive value at the first-weight that is greater than that of the second-weight of the feature-insensitive value in response to at least one distance between the distinguished home and at least one of the set of geographic features satisfying a threshold distance and weighting the feature-sensitive value at the first-weight that is less than that of the second-weight of feature-insensitive value in response to the distance between the distinguished home at each of the set of geographic features not meeting the threshold distance
updating at least one of the first-weight or the second-weight by generating an error value using a sale price of the distinguished home and the estimated value of the distinguished home; and
generating an updated estimated value for the distinguished home by reapplying the meta-model to the accessed attributes and the information identifying the set of geographic features that are near the distinguished home, such that reapplying the meta-model comprises using the at least one updated first-weight or the second weight as determined by the distance-dependent weighting function,
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claims 34, 36, 41, 43, 62 are directed towards descriptive subject matter describing the rules, parameters, and/or techniques that are used for the comparison to estimate the value of a home.
Claims 35, 37 are directed to descriptive subject matter.
Claims 38, 39, 40, 63, 66 are directed towards the recitation of generic technology at a high level of generality and applying them to the abstract idea.
Claim 44 is directed to extra-solution activities, in this case, displaying information.
Claim 46 is directed towards collecting and organizing information.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for calculating an estimated value of a home. Accordingly, the claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 11/26/2025 have been fully considered but they are not persuasive.
Claim Objections
The prior claim objections have been withdrawn due to amendments.
A new claim objection has been provided.
Rejection under 35 USC 112(b)
The prior rejections under 35 USC 112(b) have been withdrawn due to amendments.
A new rejection under 35 USC 112(b) has been provided due to amendments.
Rejection under 35 USC 112(a)
A new rejection under 35 USC 112(a) has been provided due to amendments.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
As was previously discussed in the Final Rejection mailed on 3/28/2025, reciting a meta-model and describing the models that are included in the meta-model are insufficient to overcoming the rejection for the similar reasons discussed of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2, and not Example 47, Claim 3. The claimed invention is not analogous to Example 47, Claim 3, because it is not directed towards improving technology, utilizing the results of machine learning to improve technology, deeply rooted in technology, nor directed towards steps/functions that cannot be practically performed by a human(s), e.g., inter alia, utilizing the results of machine learning to prevent the transmission of malicious data packets during transmit.
Although the claims recite “heat-map model”, “feature-sensitive model”, “feature-insensitive model” and “meta-model”, the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying the “heat-map model”, “feature-sensitive model”, “feature-insensitive model” and “meta-model” are simply application of computer models, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being “feature-sensitive model”, “feature-insensitive model” and “meta-model” are a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards real estate property evaluation and the data associated with real estate properties that can affect a property’s value so that a mathematical calculation can be performed to estimate the value of a particular real estate property based on a comparison against other real estate properties. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and re-training (i.e. updating, “dynamic, self-adjusting computation process”, and the like) are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f).
The Examiner asserts that the applicant is not improving upon or resolving an issue that arose in machine learning, machine learning training, or meta-models and simply utilizing a meta-model for the known advantages that they provide, e.g., increased accuracy, error reduction, efficiency, speed, and etc. The claimed invention is not concerned with improving meta-models nor is it concerned with how sub-models/models are being fed into a meta-model. The claimed invention is directed towards the generic technique of how models and a meta-model function or are trained and applying it to the abstract idea.
As stated above, the claimed invention is not concerned with improving technology, resolving an issue that arose in technology, or deeply rooted in technology, but reciting generic technology at a high level of generality and applying it to the abstract idea to perform mathematical calculations that a human can perform by collecting and comparing information regarding home valuations and, based on a rule(s), e.g., proximity to a geographical feature, determine the value of a particular home based on how well it compared to other homes. The sub-models are mathematical and/or data collection techniques that convey, tabulate, provide, or the like the value of homes based on various parameters so that a comparison can be made of a home of interest that best matches this pool of data to predict how much the particular home should be or is valued at. The specification does not support that the invention is directed towards improving technology, but discussing the type of information that can be inputted into a plurality of models to output a corresponding result. The invention is not directed towards the actual improvement of machine learning training or meta-models, but reciting the plurality of models at a high level of generality and applying them to the abstract idea to determine or calculate the value of a particular home based on how it compares to other similar homes and then describing the data that is used.
Further, the applicant’s argument based on Desjardins is improper and unpersuasive (see, at least, Page 16, III., Paragraph 2). Specifically, the argument is directed towards how information is stored/maintained by the system and attempting to establish that this is an improvement. However, again, unlike Desjardins, the specification is not directed towards the improvement of technology, let alone, memory usage, but directed towards real estate valuation and applying generic machine learning to perform the valuation, which can be performed by a human in their mind and/or with the aid of pen and paper, as was discussed above.
As was discussed in the Non-Final Rejection mailed on 7/30/2025, the Examiner asserts that the applicant is not improving upon or resolving an issue that arose in machine learning, machine learning training, or meta-models and simply utilizing a meta-model for the known advantages that they provide, e.g., increased accuracy, error reduction, efficiency, speed, and etc. The claimed invention is not concerned with improving meta-models nor is it concerned with how sub-models/models are being fed into a meta-model. The claimed invention is directed towards the generic technique of how models and a meta-model function or are trained and applying it to the abstract idea.
McRo is not applicable as the claimed invention is not directed towards improving computer animated lip synchronization or, more specifically, improving and resolving an issue that arose in technology nor is it deeply rooted in technology, in this case, McRo identified issues in computer animated lip synchronization, which is deeply rooted in technology, and provided techniques to improve/resolve these issues.
Similarly, BASCOM is not applicable. BASCOM identified issues regarding processing requests over the Internet and provided improved filtering techniques, which resulted in BASCOM identifying the existing issues in the technology and providing a solution to improve/resolve these issues.
Finally, the applicant’s argument toward providing evidence, i.e. Berkheimer analysis, is unpersuasive and not applicable because the Examiner never made an analysis directed towards “well-understood, routine, and/or conventional”.
Therefore, the claimed invention is not concerned with improving technology, resolving an issue that arose in technology, or deeply rooted in technology, but reciting generic technology at a high level of generality and applying it to the abstract idea.
Rejection under 35 USC 102/103
Applicant’s arguments, see Page 17, filed 11/26/2025, with respect to the rejection under 35 USC 103 with respect to Daga, have been fully considered and are persuasive. The rejection has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Zhang et al. (A Study on Automated Valuation Model in Mass Appraisal System for Real Property Tax) – which is directed towards real estate property appraisal using models
Amresh et al. (UAV Sensor Operator Training Enhancement Through Heat Map Analysis) – which is directed towards the various uses of heat maps
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 GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 1/5/2026