DETAILED ACTION
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 .
Election/Restrictions
Claims 12-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Inventions, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 05/06/2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Garrido et al. (U.S. 2019/0299924 A1) in view of Rutelin et al. (U.S. 2025/0033540 A1).
Claim 1, Garrido teaches:
A computer-implemented method for analyzing child safety seat position data in a vehicle (Garrido, Figs. 3, 5, and 6), the computer-implemented method performed by one or more processors (Garrido, Fig. 6: 180) of a computing system (Garrido, Figs. 5 and 6) in communication with one or more data sources (Garrido, Fig. 6: 180, Paragraph [0043], The plurality of devices connected to MCU 180, e.g. temp sensor 190, are data sources.), the computer-implemented method comprising:
receiving, by the one or more processors, baseline data from one or more data stores, wherein the baseline data includes child safety seat data, vehicle data, and/or child biometric data (Garrido, Paragraph [0044], The microprocessor 180 can determine whether the seating component is not installed properly, is working properly, and if a hazard exists. The conditions are based on known data, including temperature and temperature threshold, child weight, and vehicle speed (see Garrido, Paragraph [0030]), wherein the known data are examples of baseline data.);
receiving, by the one or more processors, dynamic data from a plurality of sensors, wherein at least one of the plurality of sensors is coupled to a child safety seat (Garrido, Fig. 6: 180, Paragraphs [0030] and [0043]);
inputting, by the one or more processors, the baseline data and the dynamic data, wherein the utility model (Garrido, Paragraph [0029]) is configured to determine a recommended adjustment for the child safety seat (Garrido, Paragraphs [0034-0036], The system can perform a pre-flight check, the transportation can continued to monitor itself during a trip, and after the trip is complete, and generate a corresponding alarm and/or take mitigation actions if any alarm condition is met. The corresponding alarm to an alarm condition is equivalent to a recommended adjustment, because the transportation system warns a user of an issue with the transportation system with which the user must address. For example, when a temperature exceeds a threshold temperature, the corresponding alarm regarding the temperature is equivalent to a recommendation to the user that the temperature must be addressed.);
generating, by the one or more processors, an alert including the recommended adjustment (Garrido, Paragraph [0045]); and
outputting, by the one or more processors, the alert via a user interface of a mobile device (Garrido, Paragraph [0045], Microprocessor 180 can initiate communication with remotely located mobile device 110 that a problem condition exists.).
Garrido does not specifically teach:
Inputting, by the one or more processors, the baseline data and the dynamic data into a machine-learning model, wherein the machine-learning model is configured to determine a recommended adjustment for the child safety seat; and
in response to the inputting, receiving, by the one or more processors, a recommended adjustment from the machine-learning model.
Rutelin teaches:
Inputting, by the one or more processors, the baseline data (Rutelin, Fig. 9C: 904) and the dynamic data (Rutelin, Fig. 9C: 906, 908) into a machine-learning model (Rutelin, Fig. 9C: 902), wherein the machine-learning model is configured to determine a recommended adjustment for the child safety seat (Rutelin, Paragraphs [0360-0361], Example recommended adjustments include seat belt tautness or improper installation of the child safety seat.); and
in response to the inputting, receiving, by the one or more processors, a recommended adjustment from the machine-learning model (Rutelin, Fig. 10, Paragraph [0365], At step 1012, an alert is generated.).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system in Garrido by integrating the teaching of a machine learning model, as taught by Rutelin.
The motivation would be to improve the safety of child safety seats by predicting events due to improper installation (see Rutelin, Paragraphs [0360-0361]).
Claim 2, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 1, wherein the received dynamic data comprises an ambient temperature at the child safety seat (Garrido, Paragraph [0047]).
Claim 3, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 2, further comprising:
receiving, by the one or more processors, from the machine-learning model, an indication that the ambient temperature is above a threshold (Garrido, Paragraph [0047], In the combination of Garrido in view of Rutelin, the temperature data is used with the machine learning model 902 of Rutelin.), wherein the indication includes a recommended thermoset adjustment of the vehicle (Garrido, Paragraph [0047], With respect to a temperature exceeding a pre-determined threshold level, it would have been obvious to one of ordinary skill in the art, at the time of filing, for an alert regarding temperature to inform a user to make temperature adjustments, e.g. via a thermostat of the vehicle. Additionally, Rutelin teaches generating guiding instructions based on temperature sensor data 908 (see Rutelin, Paragraph [0359]).).
Claim 4, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 1, wherein the received dynamic data includes occupied seat data, wherein the occupied seat data includes one or more corresponding occupied seat locations (Garrido, Paragraph [0051], The occupancy of the child safety seat is one or more corresponding occupied seat locations.).
Claim 5, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 4, the method further comprising:
generating, by the one or more processors, an optimized position for the child safety seat within the vehicle based upon the occupied seat data (Rutelin, Paragraph [0213], The system receives data of the child safety seat which includes a suitable position for installation and provides step-by-step instructions for a child safety seat when the seat is occupied.); and
updating, by the one or more processors, the alert based upon the optimized position for the child safety seat (Rutelin, Paragraph [0256], The child seat position is continuously monitored, and ongoing feedback regarding the position is provided as necessary.).
Claim 7, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 1, wherein the child safety seat data includes expiration data, brand data, position data, and/or dimension data (Rutelin, Paragraph [0227], The processor 202 is configured to determine if the safety seat is not suitable due to its expiry date on the materials or damage to the child safety seat. Other data regarding the child safety seat, including model make, year of manufacture, size, shape, and weight may be retrieved (see Rutelin, Paragraph [0155]).).
Claim 8, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 7, further comprising:
analyzing, by the one or more processors, the expiration data to determine that the child safety seat data exceeds an expiration threshold (Rutelin, Paragraph [0227], The processor 202 can determine whether the current safety seat is not suitable due to its expiry date.); and
in response to determining that the child safety seat data exceeds the expiration threshold, receiving, by the one or more processors a replacement child safety seat recommendation from the machine-learning model (Rutelin, Paragraph [0227], If the safety seat has reached its expiry date, the processor 202 recommends a suitable safety seat for the child. In the combination of Garrido in view of Rutelin, determinations are made via a machine learning model (see Rutelin, Fig. 9C, Paragraphs [0360-0361]).).
Claim 9, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 1, further comprising:
creating, by the one or more processors, child profile data that includes the received baseline data and the received dynamic data (Rutelin, Paragraphs [0179-0182], Dynamic data includes a measured child height and weight, and the recommended tautness is an example of a baseline data for the child safety seat. Together, the data regarding the child based on the child's weight, height, and behavior is equivalent to a child profile specific to the detected child.); and
storing the child profile data in the one or more data stores (Rutelin, Paragraph [0191], All data may be uploaded to the cloud or stored locally.).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Garrido et al. (U.S. 2019/0299924 A1) in view of Rutelin et al. (U.S. 2025/0033540 A1) in view of Schoenberg (U.S. 2014/0085070 A1).
Claim 6, Garrido in view of Rutelin further teaches:
The computer-implemented method of claim 1, wherein the child biometric data includes child position data, child length data, and/or child weight data (Rutelin, Paragraphs [0156], [0179], and [0182]).
Garrido in view of Rutelin does not specifically teach:
Wherein the vehicle data includes vehicle date data, vehicle make data, and/or vehicle model data.
Schoenberg teaches:
Wherein the vehicle data includes vehicle date data, vehicle make data, and/or vehicle model data (Schoenberg, Paragraph [0068]).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system in Garrido in view of Rutelin by integrating the teaching of make and model of a vehicle, as taught by Schoenberg.
The motivation would be to ensure the installation properties of the car seats, e.g. predetermined axes values, are correct for a given vehicle (see Schoenberg, Paragraph [0068]).
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Garrido et al. (U.S. 2019/0299924 A1) in view of Rutelin et al. (U.S. 2025/0033540 A1) in view of Ali (U.S. 2018/0281627 A1).
Claim 10, Garrido in view of Rutelin teaches:
A computer-implemented method of claim 1, further comprising:
receiving, by the one or more processors, accident data from the vehicle (Garrido, Paragraphs [0030-0031]).
Garrido in view of Rutelin does not specifically teach:
In response to receiving the accident data, determining, by the one or more processors, child safety seat condition data, wherein the child safety seat condition data includes at least one of: a low condition, a moderate condition, and/or a high condition, wherein the low condition indicates that the child safety seat is not adequate for use, the moderate condition indicates that the child safety seat is adequate for use, and the high condition indicates that the child safety seat is optimal for use.
Ali teaches:
In response to receiving the accident data, determining, by the one or more processors, child safety seat condition data, wherein the child safety seat condition data includes at least one of: a low condition, a moderate condition, and/or a high condition, wherein the low condition indicates that the child safety seat is not adequate for use, the moderate condition indicates that the child safety seat is adequate for use, and the high condition indicates that the child safety seat is optimal for use (Ali, Paragraphs [0224-0228], The system determines whether the car seat is safe to use or if some electronic or mechanical or structural components have failed and are no longer safe, which represent a moderate and low condition, respectively.).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify the system in Garrido in view of Rutelin, by integrating the teaching of an automated safety check, as taught by Ali.
The motivation would be to improve the safety of the use of a car seat by providing data from the car seat to manufacturer, distributor, or dealers to take appropriate measures (see Ali, Paragraphs [0224-0228]).
Claim 11, Garrido in view of Rutelin in view of Ali further teaches:
The computer-implemented method of claim 10, further comprising:
analyzing, by the one or more processors, via the machine-learning model, the child safety seat condition data to determine that the child safety seat has a low condition (Ali, Paragraphs [0224-0228], In the combination of Garrido in view of Rutelin in view of Ali, the machine learning model is used to analyze data (see Rutelin, Fig. 9C: 902).);
in response to determining that the child safety seat has a low condition, receiving, by the one or more processors, a recommended replacement child safety seat from the machine-learning model (Ali, Paragraphs [0224-0228], An alert is generated for the caregiver whether the seat is safe to use.); and
outputting, by the one or more processors, an updated alert via the user interface of the mobile device, wherein the updated alert includes the low condition and the recommended replacement child safety seat (Ali, Paragraphs [0224-0228], If the system determines that the car seat is no longer safe to use, it would have been obvious to one of ordinary skill in the art, at the time of filing, for the corresponding alert to indicate to the caregiver that the car seat needs to be replaced.).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES J YANG whose telephone number is (571)270-5170. The examiner can normally be reached 9:30am-6:00p M-F.
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/JAMES J YANG/ Primary Examiner, Art Unit 2686
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