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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 8, 2026 has been entered.
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 1, 2, 4, 8, 9, 11, 15, 16, 18 have been amended.
Claims 3, 10, 17 have been cancelled.
No claims have been added.
Claims 1 – 20 have been rejected under 35 USC 101.
Claims 1 – 20 have not been rejected in view of prior art. Although the prior art of record found in the PTO-892 disclose many elements of the claimed invention, the prior art of record fails to provide sufficient motivation or reasoning that would result in one of ordinary skill in the art combining the references to arrive at the claimed invention without having to rely on hindsight reasoning. In this case, although the prior art of record demonstrates that it is known to train a machine learning algorithm to predict when a vehicle part requires replacement, providing a user with a notification that a part should be replaced, and that it is known that child seats require replacement in response to a child’s growth, there is insufficient reasoning or motivation to train a machine learning algorithm to predict when a child vehicle seat will require replacement and providing an indication of the prediction. There is insufficient reasoning or motivation to substitute a vehicle part, e.g., oil change, tire, other wear and tear parts, or parts after an incident (e.g., vehicle accident) with a vehicle child safety seat, without having to rely on hindsight rationale.
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, 2, 4 – 9, 11 – 16, 18 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
predicting the predictive replacement time to replace one or more vehicle seats using (i) a set of characteristics of a previously recommended vehicle seat, (ii) replacement times for the previously recommended vehicle seat, and (iii) at least one of a user height or a user weight, wherein [predicting] includes:
determining a validation metric by comparing an output to the validation data; and
when the validation metric does not meet a threshold value, [re-evaluating the prediction];
predicting a grown rate of a user;
receiving input data related to the previously recommended vehicle seat;
determining a set of characteristics of the input data;
determining, using historical growth data, the growth rate of the user;
determining, based upon the growth rate for the user, the at least one of the user height or the user weight;
applying the set of characteristics of the input data, and the at least one of the user height or the user weight to determine the predictive replacement time for replacing the one or more vehicle seats; and
providing an indication of the predictive replacement time for display on a client device
The invention is directed towards the abstract idea of customer product notification and upgrade notice, which is further based on the collection and comparison of information and, based on a rule, identify options, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., having a user review product and customer information and comparing it against actual/currently used product and customer information and, based on a rule(s) (Has a child reached a certain age, height, weight? Has the vehicle seat manufacturer provided any information related to vehicle seat replacement? and etc.), identify option, in this case, is it time to replace a vehicle seat and, if so, notifying a customer that their vehicle seat should be replaced.
The limitations of:
predicting the predictive replacement time to replace one or more vehicle seats using (i) a set of characteristics of a previously recommended vehicle seat, (ii) replacement times for the previously recommended vehicle seat, and (iii) at least one of a user height or a user weight, wherein [predicting] includes:
determining a validation metric by comparing an output to the validation data; and
when the validation metric does not meet a threshold value, [re-evaluating the prediction];
predicting a grown rate of a user;
receiving input data related to the previously recommended vehicle seat;
determining a set of characteristics of the input data;
determining, using historical growth data, the growth rate of the user;
determining, based upon the growth rate for the user, the at least one of the user height or the user weight;
applying the set of characteristics of the input data, and the at least one of the user height or the user weight to determine the predictive replacement time for replacing the one or more vehicle seats; and
providing an indication of the predictive replacement time for display on a client device
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 and generic machine learning model. That is, other than reciting a generic processor executing computer code stored on a computer medium and generic machine learning model 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 and generic machine learning model in the context of this claim encompasses a user reviewing product material, e.g., vehicle child safety seat specifications, child information, determining whether the product requires replacement as it is no longer suitable for the end user, e.g., the child has outgrown the seat, and notifying a user, e.g., parent/guardian, that the seat will require replacement. 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 and generic machine learning model, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” 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 and generic machine learning model to communicate, store, and display (provide) information, as well as performing operations that a human can perform in their mind and/or pen and paper, i.e. collecting and comparing vehicle seat and child information against known safety information and, based on a rule(s), identify options, i.e. should the vehicle seat be replaced. The generic processor executing computer code stored on a computer medium and generic machine learning model 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 and generic machine learning model can perform the insignificant extra solution steps of communicating, storing, and displaying (providing) information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium and generic machine learning model are merely being applied to perform the steps that can be performed in the human mind and/or with the aid of 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 and generic machine learning model.
Although the claim recites “training” multiple machine learning models, “wherein training the first machine learning model includes: determining a validation metric of the first machine learning model by comparing an output of the first machine learning model to validation data, and when the validation metric does not meet a threshold value, retraining the first machine learning model, and “applying” multiple machine learning 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 in its normal, routine, and ordinary capacity 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, retraining, and applying machine learning models are simply an application of a computer model, itself an abstract idea manifestation. Further, such training, retraining, and applying of models is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is 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 reviewing vehicle seat and child information to determine if the child has outgrown their vehicle seat and, if so, providing an indication that the vehicle seat should be replaced. 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 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 use of a separate machine learning model is not an improvement to machine learning, but the utilization of a second generic machine learning model recited at a high level of generality and applying it to the abstract idea. The use of a second machine learning model is directed towards reciting and applying the technology for the benefits that it provides, i.e. faster, more efficient, and the like.
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 model(s). 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 and generic machine learning model to perform the steps of:
predicting the predictive replacement time to replace one or more vehicle seats using (i) a set of characteristics of a previously recommended vehicle seat, (ii) replacement times for the previously recommended vehicle seat, and (iii) at least one of a user height or a user weight, wherein [predicting] includes:
determining a validation metric by comparing an output to the validation data; and
when the validation metric does not meet a threshold value, [re-evaluating the prediction];
predicting a grown rate of a user;
receiving input data related to the previously recommended vehicle seat;
determining a set of characteristics of the input data;
determining, using historical growth data, the growth rate of the user;
determining, based upon the growth rate for the user, the at least one of the user height or the user weight;
applying the set of characteristics of the input data, and the at least one of the user height or the user weight to determine the predictive replacement time for replacing the one or more vehicle seats; and
providing an indication of the predictive replacement time for display on a client device
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:
Claim 2 is directed towards descriptive subject matter, in this case, describing what the characteristics, input data, child data, vehicle data, current vehicle seat data, and location data are intended to include.
Claim 4 is directed towards descriptive subject matter, in this case, describing what the vehicle seat data and on-market vehicle seat data are intended to include, as well as the collection and comparison of information, as well as “Mathematical Concepts”, in this case, generating/calculating a vehicle seat recommendation score and ranking the seats based on the score in order to perform the human activity of generating/writing a recommendation list.
Claim 5 is directed towards the collection and comparison of information and, based on a rule(s), identify options, in this case, collecting store location information and comparing them against the user’s location to identify the closes store that sells a replacement vehicle set and performing the extra-solution activity of presenting/displaying an organized/sorted list of stores, which is further based on the collection and organization of information.
Claim 6 is directed towards the collection and comparison of information and, based on a rule(s), identify options, in this case, predicting a replacement time by reviewing product material, e.g., vehicle child safety seat specifications, child information, determining whether the product requires replacement as it is no longer suitable for the end user, e.g., the child has outgrown the seat, and notifying a user, e.g., parent/guardian, that the seat will require replacement, as well as determining the accuracy replacement time based on the assessment, which can also encompass “Mathematical Concepts”.
Claim 7 is directed towards “Mathematical Concepts” as it is directed towards reducing the percent rate of error and generating/calculating a confidence interval based on the information, analysis, and results of claim 6.
The remaining claims are directed towards subject matter that has already been discussed above.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for notifying a user that it is time to replace a vehicle seat. Accordingly, the claims are not patent eligible.
Response to Arguments
Applicant's arguments filed 1/8/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The Examiner asserts that Ex parte Desjardins does not apply to the instant claimed invention. Unlike Desjardins, which was directed towards identifying issues that arose in machine learning technology, providing a resolution for the identified issue, and deeply rooted in machine learning, the claimed invention is directed towards customer product notification and upgrade notice, which is further based on the collection and comparison of information and, based on a rule, identify options, in this case, whether to notify a customer that they should upgrade their child seat based on characteristics of a child and what the seat is designed to hold. The amendments are insufficient to overcome the rejection and are not analogous to Desjardins because, as stated above, the claimed invention is not directed towards the same or similar issues of why Desjardins was found to be patent eligible and the amendments are simply directed towards retraining a machine learning model when an undesirable output has been provided based on how the output compares against known or expected information.
Accordingly, the claimed invention is similar to 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, Example 47, Claim 2, because the claimed invention is not directed towards improving technology, resolving an issue that arose in technology, or deeply rooted in technology. The claimed invention is reciting generic machine learning at a high level of generality and applying it to the abstract idea. The use of a separate machine learning model is not an improvement to machine learning, but the utilization of a second generic machine learning model recited at a high level of generality and applying it to the abstract idea. The use of a second machine learning model is directed towards reciting and applying the technology for the benefits that it provides, i.e. faster, more efficient, and the like.
The applicant’s arguments and claimed invention are not directed towards improving machine learning, resolving an issue that arose in machine learning, or deeply rooted in machine learning, but directed towards describing the data that comprises the training data while still reciting and applying generic training techniques to generic machine learning models to perform operations that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper, e.g., having a user review product and customer information and comparing it against actual/currently used product and customer information and, based on a rule(s) (Has a child reached a certain age, height, weight? Has the vehicle seat manufacturer provided any information related to vehicle seat replacement? and etc.), identify option, in this case, is it time to replace a vehicle seat and, if so, notifying a customer that their vehicle seat should be replaced.
The claimed invention is not providing a technological improvement, but reciting and applying generic technology to perform steps that can be performed by a human(s), in the human mind, and/or with the aid of pen and paper while failing to improve the technology, resolve an issue that arose in the technology, or deeply rooted in technology.
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.
PishPosh (Infant Car Seat Comparison Chart); North Providence Police Department (Child Safety Seat Chart) – which provide charts and guidelines of when to change a child car seat based on a child’s characteristics
Durbin (Technical Report--Child Passenger) – which provide charts and guidelines of when to change a child car seat based on a child’s characteristics, as well as child car seat usage
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 2/9/2026