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 .
Status of Claims
This Office Action is in response to the application filed on December 20, 2024. Claims 1-13 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDSs) were submitted on December 23, 2024 and March 31, 2025. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Examiner acknowledges Applicant’s request for priority to U.S. App. No. 18/597,400 dated March 6, 2024.
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 1-13 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 is directed toward a system, claim 7 is directed toward a method, and claim 13 is directed toward a non-transitory computer readable medium (i.e., apparatus). Therefore, each of the independent claims 1, 7, and 13 along with the corresponding dependent claims 2-6 and 8-12 are directed to a statutory category of invention under Step 1.
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claims 1, 7, and 13 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes. The language of independent claim 7 is used for illustration:
A method for determining the load state of a vehicle, the method comprising operating at least one processor to:
receive vehicle data associated with one or more vehicles (receiving data is considered insignificant extra-solution activity, as explained further below);
identify one or more vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data (a person may observe vehicle data and identify one or more vehicle maneuvers based on the data. For example, IMU data showing a vehicle turn); and
determine the load state of the vehicle by inputting into at least one machine learning model each portion of vehicle data associated with the one or more vehicle maneuvers, the at least one machine learning model trained to determine a load state of a vehicle based on training data associated with a plurality of previous vehicle maneuvers (this is considered apply-it level use of a generic computer, as explained further below).
As explained above, independent claim 7 recites at least one abstract idea. The other independent claims 1 and 13, which are of similar scope to claim 7, likewise recite at least one abstract idea under Step 2A, Prong 1.
Under Step 2A, Prong 2, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”; see at least MPEP 2106.04(d).
In this case, the mental processes judicial exception is not integrated into a practical application. For example, independent claims 1, 7, and 13 recite the additional elements of a non-transitory computer readable medium…, at least one data storage…, at least one processor…, receive vehicle data…, and inputting into at least one machine learning model…. These limitations amount to implementing the abstract idea on a computer, add insignificant extra solution activity, and/or generally link use of the judicial exception to a particular technological environment or field of use; see at least MPEP 2106.04(d). More specifically,
a. a non-transitory computer readable medium… found in independent claim 13. This limitation amounts to merely implementing the abstract idea via a generic computer.
b. at least one data storage… found in independent claim 1. This limitation amounts to merely implementing the abstract idea via a generic computer.
c. at least one processor… found in independent claims 1 and 7. This limitation amounts to merely implementing the abstract idea via a generic computer.
d. receive vehicle data… found in independent claim(s) 7 and 13. This limitation is considered insignificant extra-solution activity, as explained further below.
e. inputting into at least one machine learning model… found in independent claims 1, 7, and 13. This limitation amounts to merely implementing the abstract idea via a generic computer due to it merely invoking a computer as a tool to perform an existing process. Specifically, it uses software to tailor information for output to a user which the court in Intellectual Ventures I LLC v. Capital One Bank (USA) found to be insufficient; see at least Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015).
Therefore, taken alone, the additional elements do not integrate the abstract idea into a practical application. Furthermore, looking at the additional limitation(s) as an ordered combination or as a whole, the limitations add nothing significant that is not already present when looking at the elements taken individually. Because the additional elements, do not integrate the abstract idea into a practical application by imposing meaningful limits on practicing the abstract idea, independent claims 1, 7, and 13 are directed to an abstract idea.
Under Step 2B, the claims do not include any 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 in Step 2A, Prong Two, the additional element of limiting the use of the idea to one particular environment employs generic computer functions to execute an abstract idea and, therefore, does not add significantly more. Limiting the use of the abstract idea to a particular environment or field of use cannot provide an inventive concept. Additionally, as discussed above, the limitations of *, *, *, as recited above, are considered insignificant extra solution activities.
A conclusion that an additional element is insignificant extra solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitation of receive vehicle data… is well-understood, routine, and conventional activities, because they have all been deemed insignificant extra solution activity by one or more Courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
a. receive vehicle data… is considered well-understood, routine, and conventional activity under at least buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claims 1, 7, and 13 are patent ineligible under 35 U.S.C. 101.
Dependent claims 2-6 and 8-12 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent claims 2-6 and 8-12, when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. § 101 based on same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2-6 and 8-12 are patent ineligible. Therefore, claims 1-13 are patent ineligible under 35 U.S.C. 101.
Examiner encourages Applicant to set an interview to discuss potential amendments for overcoming the above rejections under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Pub. No. 2025/0074432 (hereinafter, “Lee”).
Regarding claim 1, Lee discloses A system for determining a load state of a vehicle (see at least [0053], [0061], and the publication generally), the system comprising:
at least one data storage operable to store vehicle data associated with one or more vehicles (see at least [0122]-[0125]); and
at least one processor in communication with the at least one data storage (see at least [0122]-[0125]), the at least one processor operable to:
identify one or more vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data (see at least [0023]-[0024], and [0107]; change in wheel torque, vehicle velocity, and vehicle acceleration may be considered vehicle maneuvers, and the maneuvers are associated with vehicle data. Examiner notes that, under BRI, any vehicle movement may be considered a vehicle maneuver); and
determine the load state of the vehicle by inputting into at least one machine learning model each portion of vehicle data associated with the one or more vehicle maneuvers, the at least one machine learning model trained to determine a load state of a vehicle based on training data associated with a plurality of previous vehicle maneuvers (see at least Fig. 3, [0023]-[0024], and [0070]-[0077]; a neural network (i.e., ML model) may be used to determine the load state of the vehicle based on collected vehicle data including vehicle acceleration and velocity values (i.e., vehicle maneuvers)).
Claims 7 and 13 are rejected under essentially the same reasoning as claim 1. Additionally, Lee discloses a non-transitory computer readable medium at least at [0123].
Regarding claim 2, Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein each portion of vehicle data comprises geospatial data, vehicle engine data, or a combination thereof from a duration of the vehicle maneuver (see at least [0089] and [0107]; geospatial data may be used such as, for example, velocity and acceleration over time of the vehicle).
Claim 8 is rejected under essentially the same reasoning as claim 2.
Regarding claim 3, Lee discloses all of the limitations of claim 2. Additionally, Lee discloses wherein each portion of vehicle data comprises normalized accumulated RPM, a normalized accumulated torque, energy, a normalized difference in speed, a normalized difference in elevation, a mean acceleration, a normalized number of gear changes, a change in speed over a selected subsection of time, a change in elevation over a selected subsection of time, or a combination thereof (see at least Fig. 2, Fig. 6A, [0117]-[0120]; the gradient change over time may be used).
Claim 9 is rejected under essentially the same reasoning as claim 3.
Regarding claim 4, Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the at least one machine learning model is trained to determine the load state of the vehicle based on training data that comprises an estimated vehicle weight value, a measured vehicle weight value, or a combination thereof associated with at least one of the plurality of vehicle maneuvers (see at least [0076]-[0078]; the machine learning model may be trained for the load state of the vehicle based on vehicle weights which are necessarily estimated or measured and based on the acceleration/velocity of the vehicle).
Claim 10 is rejected under essentially the same reasoning as claim 4.
Regarding claim 5, Lee discloses all of the limitations of claim 4. Additionally, Lee discloses wherein an error associated with the estimated weight value is less than or equal to 25% based at least in part on the measured vehicle weight value (see at least [0117]; in at least some cases, the estimated weight value error may be less than or equal to 25% (e.g., 200/2400=8.3%)).
Claim 11 is rejected under essentially the same reasoning as claim 5.
Regarding claim 6, Lee discloses all of the limitations of claim 1. Additionally, Lee discloses wherein the at least one machine learning model comprises a Random Forest model, an AutoEncoder, an AutoInt model, a Tabnet model, or a combination thereof (see at least [0072] and [0075]-[0076]; the machine learning model may be trained via unsupervised learning (i.e., may include an autoencoder)).
Claim 12 is rejected under essentially the same reasoning as claim 6.
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
U.S. Pub. No. 2024/0317242 which relates to estimating a vehicle weight based on driving data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIFFANY P YOUNG whose telephone number is (313)446-6575. The examiner can normally be reached M-R 6:30 AM- 4:30 PM.
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, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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TIFFANY YOUNG
Primary Examiner
Art Unit 3666
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3666