E
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In claim 1, the “vehicle driving information provider” in the limitation “receiving vehicle driving information input while the vehicle is traveling from a vehicle driving information provider” invokes 112(f) as “provider” is a term that does not have definite structure which enables the provision/transmission of data.
In claim 1, the “driving image information provider” in the limitation “receiving driving image information input while the vehicle is traveling from a driving image information provider” invokes 112(f) as “provider” is a term that does not have definite structure which enables the provision/transmission of data.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Regarding the vehicle driving information provider, a review of the specification (paragraph 0058) shows that the following appears to be the corresponding structure to these claim limitations:
"In order to provide the vehicle driving information, the vehicle driving information provider 100 may include: an acceleration sensor capable of measuring lateral and/or longitudinal acceleration; an angular velocity sensor capable of measuring the angular velocity of the roll rate, the pitch rate, and/or the yaw rate; a steering angle sensor; an accelerator pedal sensor (APS); a brake pedal sensor (BPS); a vehicle speed sensor; a lane recognition sensor; and the like. However, a configuration of the vehicle driving information provider 100 is not limited thereto." (Emphasis added.)
Regarding the driving image information provider, a review of the specification (paragraph 0060) shows that the following appears to be the corresponding structure to these claim limitations:
"In order to provide the driving image information, the driving image information provider 200 may include a Light Detection and Ranging (LiDAR) sensor, a camera, a Radio Detecting and Ranging (RaDAR) sensor, an ultrasound sensor, and the like. However, a configuration of the driving image information provider 200 is not limited thereto." (Emphasis added.)
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-5, 7-8, 10, 12-14, 16, 18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the following:
extracting, by a first feature extraction module implemented by the processor, a first feature vector from the vehicle driving information;
extracting, by a second feature extraction module implemented by the processor, a second feature vector from the driving image information;
fusing, by a fusion module implemented by the processor, the first and second feature vectors to generate fusion data;
predicting, by a driving route prediction module implemented by the processor, route of the vehicle based on the fusion data; and
outputting, by the driving route prediction module, route prediction information of the vehicle,
wherein extracting the second feature vector comprises extracting the second feature vector in a two-dimensional (2D) array from the driving image information using a convolution neural network (CNN) and inputting the second feature vector in the 2D array to a dense layer to extract the second feature vector in a 1D array,
wherein outputting the route prediction information comprises inputting the fusion data to a recurrent neural network (RNN) and inputting route prediction information output from the RNN to a dense layer to output the route prediction information in a 1D array, and
wherein the driving route prediction module is trained using an objective function
θ
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for training according to the following equation that optimizes a driving route prediction result:
θ
*
=
a
r
g
max
θ
∑
t
=
0
N
log
P
(
S
t
|
I
,
S
0
,
…
S
t
-
1
;
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)
wherein St denotes a predicted route for each time point, I denotes an input image, and θ denotes an overall neural network learning parameter.
The limitations (a-e and h) recited above, as drafted, are a process that, under its broadest reasonable interpretation, covers mathematical calculations but for the recitation of generic computer components. That is, other than reciting a processor and a memory, nothing in the claimed elements precludes the steps from practically being performed using mathematical calculations. For example, both extraction steps (a-b) can require mathematical calculations such as normalization to extract feature information. The fusion step (c) can require simple concatenation along with a well-known rectified linear unit (ReLU) function to produce the fused data. The prediction and outputting steps (d-e) can utilize a variety of well-known equations such as best fit formulae to predict future data using the known data. The training step (h) requires the explicit utilization of a mathematical function. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation utilizing mathematical equations but for the recitation of generic computer components, then it falls within the "Mathematical Concepts" grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the sensors, processor, and memory is/are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using (a) generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The limitations of receiving information from the providers are insignificant extra pre-solution activities of mere data reception. Mere data reception cannot form an inventive concept. If instead the outputting step (e) is interpreted as a transmission or display of data, then the limitation of outputting the route prediction information is an insignificant extra post-solution activity of mere data transmission or mere display. Mere transmission and mere display cannot form an inventive concept. The recurrent neural network (RNN) and convolutional neural network (CNN) is/are recited at a high level of generality such that it amounts to no more than mere instructions to use the neural networks as a tool to perform the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept. See MPEP § 2106.05(f).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sensors, processor, and memory are recited at a high level of generality as detailed above. A conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, and conventional activity in the field. The limitations of receiving information from the providers and outputting route prediction information are well-understood, routine, and conventional activities because buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) indicated that the transmission and reception of data over a network is a well-understood, routine, and conventional function. If instead, the information is received/stored in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicated that the storing/retrieval of data from memory is a well-understood, routine, and conventional function. The limitation of outputting route prediction information is a further well-understood, routine, and conventional activity because Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344-45, 127 USPQ2d 1553, 1559-60 (Fed. Cir. 2018) indicated that the display of data without any limitations specifying how to achieve the desired result is a well-understood, routine, and conventional function. See MPEP § 2106(d)(II) and 2106.05(a)(I). Hence, the claims are not patent eligible.
Dependent claim(s) 2-5, 7-8, 10, 13-14, 16, 18, and 20 do(es) not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claim(s) is/are directed towards further aspects of the abstract idea.
Allowable Subject Matter
Claims 1-5, 7-8, 10, 12-14, 16, 18, and 20 would be allowable if rewritten in independent form including all limitation of the base claim and any intervening references and if rewritten to overcome the 101 rejections of record.
The following is a statement of reasons for the indication of allowable subject matter:
All claims contain the allowable subject matter indicated in the Office Action dated 10/16/2025 either within the claim itself or within a claim it depends upon.
Response to Amendment
Claim amendments filed 8/21/2026 have been received and fully considered and overcome the 112(a) new matter rejections of record detailed in the Office Action dated 5/21/2026. These/this rejections have/has been withdrawn.
Response to Arguments
Applicant's arguments filed 8/21/2026 have been fully considered but they are not persuasive.
On pages 9-10, applicant argues that the rolling up of claims 6, 9, 15, and 17 into their respective independent claims overcomes the 101 rejection (detailed in the Office Action dated 2/12/2026) by reciting the performance of the abstract idea with a plurality of neural networks. Particularly, applicant argues that the use of two encoders (the first and second feature extraction module wherein the second feature extraction module is a CNN), the fusion module, and the route prediction module (RNN) discloses a specific architecture that “constitutes a particular technical implementation… rather than a generic invocation of neural networks as a tool or a general linking of a mathematical concept to a technological environment.” Examiner respectfully reminds the applicant that MPEP § 2106.05(b) recites that a particular machine would integrate a judicial exception. Examiner posits that a collection of well known neural networks cannot be considered a particular machine, as a particular machine is generally directed towards the hardware construction of the machine. MPEP § 2106.05(b)(I) even clearly states that “It is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine.” Thus, the use of a combination of neural networks cannot be considered a particular machine and should instead be understood as a tool to perform the claimed invention. Further, purely as a matter of note, it should be noted that the claims, as written, do not recite that the first and second feature extraction modules are different encoders as the applicant alleges.
As for the combination of neural networks being recited beyond an “apply it” level, the claims, as written, are generically recited such that the various modules merely perform the claimed method without much detail regarding how each model individually or collectively performs each function. The amendment limitations attempt to provide detail on how the CNN and RNN are used to perform the functions, but this merely recites that a dense layer is used in the process without specific recitations on layer-to-layer interaction, layer-to-layer data modification, and so on. Thus, the claims merely instruct a user to use neural networks, including dense layers therewithin, to perform the claimed invention.
On pages 10-11, applicant merely argues that the claimed invention is an improvement. Examiner respectfully disagrees. The claimed invention does not recite a technical improvement to the structure or functioning of a computer. Instead, it uses a plurality of neural networks to perform a mathematical process. Even with consideration that the mathematical process requires different mathematical concepts to be performed (extraction and generation as applicant indicates), the claimed invention is still a mathematical process. Merely combining CNN and RNN to cover each other’s deficiencies is also a well known combination within the art, and thus merely combining the use of two well known neural networks cannot be considered an improvement.
Further, applicant argues that “conventional route prediction technology relies on constant turn rate and acceleration (CTRA) and constant turn rate and velocity (CTRV) physics models” wherein “the amended independent claims include the specific combination of CNN
architecture and RNN architecture described above, which can provide route predictions at more frequent time points over a given prediction horizon than the conventional model”. However, nothing in the claimed limitations precludes the utilization of CTRA and CTRV models with the claimed method. Thus, any alleged improvement over these models cannot be considered persuasive since these models are not precluded from use in the claimed invention. Further, which the invention allegedly “can provide route predictions at more frequent time points”; however, nothing in the claimed invention actually results in a production of data at a frequency not possible with the CTRA and CTRV models. I.e., the claims do not preclude data from being processed at the same, or a less frequent, interval as CTRA and CTRV models. Mere allegations that an invention can be used in a way that results in an improvement is not convincing evidence that the invention inherently does perform in a way that results in an improvement.
Thus, the arguments are not persuasive, and the 101 rejection detailed in the office action data 2/12/2026 has been restored.
Conclusion
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 Ashley Tiffany Schoech whose telephone number is (571)272-2937. The examiner can normally be reached 4:45 am - 3:15 pm PT Tuesday - Friday.
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/A.T.S./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
Prosecution Insights