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 . Claims 1, 3-8, 10-15, and 17-20 (Species II) have been elected with traverse. Claims 2, 9, and 16 (Species I) are non-elected. Claims 1-20 are currently pending. Claims 1, 3-8, 10-15, and 17-20 (Species II) have been examined in this application. This communication is the first action on the merits (FAOM).
Examiner's Note
Examiner has cited particular paragraphs/columns and line numbers or figures in the
references as applied to the claims below for the convenience of the applicant. Although the
specified citations are representative of the teachings in the art and are applied to the specific
limitations within the individual claim, other passages and figures may apply as well. It is
respectfully requested from the applicant, in preparing the responses, to fully consider the
references in their entirety as potentially teaching all or part of the claimed invention, as well as
the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is
reminded that the Examiner is entitled to give the broadest reasonable interpretation to the
language of the claims. Furthermore, the Examiner is not limited to Applicant's definition which is not specifically set forth in the disclosure.
Election/Restrictions
Applicant's election with traverse of Species II (Claims 1, 3-8, 10-14, and 17-20) in the reply filed on 8/22/2026 is acknowledged.
The traversal is on the ground(s) that:
procedurally the requirement is defective because the examiner failed to group the dependent claims corresponding to the identified species and has improperly shifted the burden of claim mapping onto the Applicant; and
the species are not mutually exclusive because paragraph [0050] of the specification explicitly provides that variants i) and ii) may be combined.
Argument (1) is not found to be persuasive. See MPEP 809.02(a) which states for a restriction between species, the following actions should be taken: “(A) Identify generic claims or indicate that no generic claims are present. (B) Clearly identify each (or in aggravated cases at least exemplary ones) of the disclosed species, to which claims are to be restricted. The species are preferably identified as the species of figures 1, 2, and 3 or the species of examples I, II, and III, respectively. In the absence of distinct figures or examples to identify the several species, the mechanical means, the particular material, or other distinguishing characteristic of the species should be stated for each species identified. If the species cannot be conveniently identified, the claims may be grouped in accordance with the species to which they are restricted. Provide reasons why the species are independent or distinct. (C) Applicant should then be required to elect a single disclosed species under 35 U.S.C. 121, and advised as to the requisites of a complete reply and their rights under 37 CFR 1.141. To be complete, a reply to a requirement made according to this section should include a proper election along with a listing of all claims readable thereon, including any claims subsequently added.” In the Requirement for Restriction/Election dated 6/25/2026 , the examiner has (A) identified generic claims on page 3 of as claims 1, 8, and 15. The examiner has also (B) clearly identified the species by figures, paragraphs of the specification, and distinguishing characteristics on pages 2-3. Species I (Specification [0040-0041], Figure 2) and Species II (Specification [0043-0049], Figures 3-4). Furthermore, see page 3 of Requirement for Restriction/Election: “Applicant is advised that the reply to this requirement to be complete must include (i) an election of a species to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected species or grouping of patentably indistinct species, including any claims subsequently added.” In this case, the species can be conveniently identified, therefore the examiner is not required to group the claims in accordance with the species, Applicant must identify the claims encompassing the species.
Argument (2) is not found persuasive. See MPEP 806.04(f) which states “Where two or more species are claimed, a requirement for restriction to a single species may be proper if the species are mutually exclusive. Claims to different species are mutually exclusive if one claim recites limitations disclosed for a first species but not a second, while a second claim recites limitations disclosed only for the second species and not the first. This may also be expressed by saying that to require restriction between claims limited to species, the claims must not overlap in scope.” In this case, each species requires limitations that are not present in the other. Species I includes limitations requiring the at least one steering variable be processed using a recurrent neural network and the at least one piece of context information using a non-recurrent neural network and combining these in at least one layer to provide the output data, while these limitations are not present in Species II. Species II includes limitations such as determining starting parameters used for the initialization, a mapping rule, repeating initialization, while these limitations are not present in Species I. Paragraph [0050] generically stating that variants may be combined, but there is not an actual embodiment that encompasses Species I and II. The specification and figures do not describe or show how an embodiment encompassing both variants would be structured or how it would function.
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 recites “herein” but should instead recite --wherein--.
Appropriate correction is required.
Claim Interpretation
Use of the word "means" ( or "step for") in a claim with functional language creates a
rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C.
112(-f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(-f) (pre-
AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with
sufficient structure, material, or acts within the claim itself to entirely perform the recited
function.
Absence of the word "means" ( or "step for") in a claim creates a rebuttable
presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(-f)
(pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(-f) (pre-AIA 35
U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function
but fails to recite sufficiently definite structure, material or acts to perform that function.
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:
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;
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
the term “means” or “step” or the generic placeholder is not modified by sufficient
structure, material, or acts for performing the claimed 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: “component” in claims 1-20 and “data processing unit” in claims 8-20.
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.
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.
The above-referenced claim limitations has/have been interpreted under 35 U.S.C.
112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because: “component” in claims 1-20 and “data processing unit” in claims 8-20 all use a generic placeholder “component” or “unit” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth
paragraph, the claims have been interpreted to cover the corresponding structure described in
the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding
structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth
paragraph limitation:
Component: [0027, 0030, 0041] – “recurrent neural network… non-recurrent neural network”
Data processing unit: [0026] - Components of the device, particularly the data processing unit, may be implemented as a combination of hardware and software, such as program code executed on a microcontroller or microprocessor. Alternatively, individual or combined components may be implemented as an application-specific integrated circuit (ASIC) and/or a field-programmable gate array (FPGA). The data processing unit may include at least one computing unit and at least one memory.
For all the units corresponding to a computer (hardware) the software (steps in an
algorithm/flowchart) should be included to indicate proper support.
If applicant wishes to provide further explanation or dispute the examiner's interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. l 12(f)
or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will
clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a
sufficient showing that the claim recites/recite sufficient structure, material, or acts for
performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C.
112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination
Guidelines for Determining Compliance With 35 U.S. C. 112 and for Treatment of Related Issues
in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 3-6, 8, 10-13, 15, and 17-20 recite “separate components” and “at least one component”. Claims 3, 10, and 17 recite “at least one component” and “the at least one component”, claims 5, 12, and 19 recite “the at least one component”, claims 6, 13, and 20 recite “a component”, “claims 7 and 14 recite “at least one component”. There is insufficient antecedent basis for some of these limitations and it is unclear if some/all of these limitations are referring to the same component or if some/all of these limitations are referring to different components. The metes and bounds of the claim limitations are vague and ill-defined, rendering the claims indefinite. As best understood, the claims will be interpreted broadly to be referring to either the same or different components.
Claims 2, 9, and 16 recite “processing the detected at least one steering variable using a recurrent neural network… processing the detected at least one piece of context information using a non-recurrent neural network”. However, claims 1, 8, and 15 recite that components perform these steps. It is unclear if the components are the same as the recurrent neural network and non-recurrent neural network or different. The metes and bounds of the claim limitations are vague and ill-defined, rendering the claims indefinite. As best understood, the claims will be interpreted such that claims 2, 9, and 16 intend to indicate that the separate components include the recurrent neural network and non-recurrent neural network.
Claims 2-7, 9-14, and 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected claims 1, 8, and 15, respectively, and for failing to cure the deficiencies listed above.
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 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, 3-6, 8, 10-13, 15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng (CN 115782892 A, cited in the IDS filed 1/30/2025, a machine translation is attached and is being relied upon) in view of Manderson (Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images).
Regarding claims 1, 8, and 15, Cheng discloses a method, a device, and a steering system for recognizing a hands-off state at a steering wheel of a vehicle (see at least [n0001, n0024] – steering wheel hands-off detection), comprising: a device configured to recognize a hands-off state at a steering wheel of a vehicle (see at least [n0001, n0024] - steering wheel hands-off detection method and device, a computer storage medium, a computer program product, a vehicle electronic control unit, and a vehicle), the device comprising: at least one steering variable sensor configured to detect at least one steering variable at the steering wheel (see at least [n0026-n0027] – steering wheel angle sensor… torque sensor measuring torque transmitted between the steering wheel and the steering gear output shaft); and a data processing unit configured to: obtain the detected at least one steering variable and at least one detected piece of context information (see at least [n0026-n0028, 0031] - a first signal from the steering wheel angle sensor, a second signal from the torque sensor, and a third signal from the wheel speed sensor are received); provide a trained machine learning model (see at least [0032] – trained model); supply the detected at least one steering variable and the detected at least one piece of context information to the trained machine learning model as input data (see at least [0032] - based on the first signal, the second signal and the third signal, a trained model is used to detect whether the driver's hands have left the steering wheel); process, by the trained machine learning model, the detected at least one steering variable and the detected at least one piece of context information to recognize a hands-off state based on at least the detected at least one steering variable and the detected at least one piece of context information and to output an associated piece of state information as output data (see at least [n0025, 0032, n0032-n0033, n0037] - based on the first signal, the second signal and the third signal, a trained model is used to detect whether the driver's hands have left the steering wheel… the trained model is a linear regression model, which can be used to output the recognition result of whether the driver's hands have left the steering wheel and the confidence level of the recognition result… sensing the identification result and the confidence level to determine whether to send a warning signal).
Cheng does not appear to explicitly disclose perform at least one of (i) processing the detected at least one steering variable and the detected at least one piece of context information using separate components of the trained machine learning model, and (ii) initializing at least one component of the trained machine learning model that processes the detected at least one steering variable based on the detected at least one piece of context information.
Manderson teaches the following limitations: perform at least one of (BRI requires only one of the following) (i) processing the detected at least one steering variable and the detected at least one piece of context information using separate components of the trained machine learning model, and (ii) initializing at least one component of the trained machine learning model that processes the detected at least one steering variable based on the detected at least one piece of context information (see at least Fig. 2 on page 1265, paragraph B. on page 1266 - The state st is composed of the current and several previous ground and aerial aligned images to increase context… The current image state st is put through a convolutional neural network to form the initial hidden state of a Long Short-Term Memory (LSTM) [33] recurrent neural network.).
To summarize, Cheng teaches a trained model processing steering wheel angle, steering torque, and vehicle speed to classify a hands-on or hands-off state of a driver. Manderson teaches a trained model that processes a state of the vehicle and action inputs such as steering angles to output a probability of terrain classes. In Manderson, images of the vehicle’s environment are put through a CNN to form the initial hidden state which is input to the trained model in order to increase context. Both Cheng and Manderson include a trained model that receives various types of input data and outputs a classification/prediction in a vehicle-specific application.
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Manderson into the invention of Cheng with a reasonable expectation of success. The motivation of doing so to provide an initial hidden state which can increase context (Manderson - Fig. 2 on page 1265, paragraph A. on page 1265, paragraph B. on page 1266). Initialization is a conventional concept in neural networks, while context dependent initialization is also known and Manderson demonstrates that context dependent initialization can increase context in vehicle-specific applications. Therefore, this context dependent initialization is a known technique that is being applied to a different field or application which is ready for improvement in order to yield predictable results.
Regarding claims 3, 10, and 17, Cheng does not appear to explicitly disclose determining starting parameters for at least one component of the trained machine learning model that processes the detected at least one steering variable based on a mapping rule; and initializing the at least one component using the determined starting parameters.
Manderson teaches the following limitations: determining starting parameters for at least one component of the trained machine learning model that processes the detected at least one steering variable; and initializing the at least one component using the determined starting parameters (see at least Fig. 2 on page 1265, paragraph B. on page 1266 - The state st is composed of the current and several previous ground and aerial aligned images to increase context… The current image state st is put through a convolutional neural network to form the initial hidden state of a Long Short-Term Memory (LSTM) [33] recurrent neural network… outputting ht to the LSTM cell as shown.).
The motivation to combine Cheng and Manderson is the same as in the rejection of claims 1, 8, and 15, above.
Regarding claims 4, 11, and 18, Cheng does not appear to explicitly disclose herein the mapping rule is provided by a trained neural network.
Manderson teaches the following limitations: herein the mapping rule is provided by a trained neural network (see at least Fig. 2 on page 1265, paragraph B. on page 1266 - The state st is composed of the current and several previous ground and aerial aligned images to increase context… The current image state st is put through a convolutional neural network to form the initial hidden state of a Long Short-Term Memory (LSTM) [33] recurrent neural network… outputting ht to the LSTM cell as shown.).
The motivation to combine Cheng and Manderson is the same as in the rejection of claims 1, 8, and 15, above.
Regarding claims 5, 12, and 19, Cheng does not appear to explicitly disclose wherein initializing the at least one component comprises initializing a recurrent neural network within the trained machine learning model, wherein the starting parameters include at least one memory of the recurrent neural network.
Manderson teaches the following limitations: wherein initializing the at least one component comprises initializing a recurrent neural network within the trained machine learning model, wherein the starting parameters include at least one memory of the recurrent neural network (see at least Fig. 2 on page 1265, paragraph B. on page 1266 - The state st is composed of the current and several previous ground and aerial aligned images to increase context… The current image state st is put through a convolutional neural network to form the initial hidden state of a Long Short-Term Memory (LSTM) [33] recurrent neural network… outputting ht to the LSTM cell as shown.).
The motivation to combine Cheng and Manderson is the same as in the rejection of claims 1, 8, and 15, above.
Regarding claims 6, 13, and 20, Cheng does not appear to explicitly disclose further comprising deactivating a component of the trained machine learning model that provides the mapping rule after initialization.
Manderson teaches the following limitations: deactivating a component of the trained machine learning model that provides the mapping rule after initialization (see at least Fig. 2 on page 1265, paragraph B. on page 1266 - The state st is composed of the current and several previous ground and aerial aligned images to increase context… The current image state st is put through a convolutional neural network to form the initial hidden state of a Long Short-Term Memory (LSTM) [33] recurrent neural network… outputting ht to the LSTM cell as shown. – the CNN is used to form the initial hidden state, subsequent processing in the LSTM using the steering actions, the CNN is repeatedly applied during processing in the LSTM – see paragraphs [0031-0032] of the specification where deactivated is described as not using or no longer required, and activated is described as using or required).
The motivation to combine Cheng and Manderson is the same as in the rejection of claims 1, 8, and 15, above.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng in view of Manderson and Vitthaladevuni (US 2022/0052827 A1).
Regarding claims 7 and 14, Cheng does not appear to explicitly disclose further comprising repeating the initialization of at least one component of the trained machine learning model that processes the at least one steering variable in response to detecting a change in the at least one piece of context information.
Manderson teaches the following limitations: repeating the initialization of at least one component of the trained machine learning model that processes the at least one steering variable (see at least paragraph A. on page 1264, Fig. 2 on page 1265 – the process shown in Fig. 2 would be iteratively performed).
The motivation to combine Cheng and Manderson is the same as in the rejection of claims 1, 8, and 15, above.
Vitthaladevuni teaches the following limitations: repeating the initialization of at least one component of the trained machine learning model that processes the at least one variable in response to detecting a change in the at least one piece of context information (see at least [0033] – if the environment changes… flush the hidden states… reset the hidden states… context information).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Vitthaladevuni into the invention of Cheng with a reasonable expectation of success. The motivation of doing so is that changes in the environment can make a subset of previous hidden states irrelevant, therefore the previous hidden states can be flushed and reset based on context information thereby improving performance (Vitthaladevuni – [0033, 0092]). Enabling the initialization to be repeated when the hidden states are irrelevant due to a change would ensure that the model does not continue to operate using hidden states that are no longer relevant because of the change. Therefore, doing so improves the accuracy of the hidden states being used.
Conclusion
The prior art made of record, and not relied upon, considered pertinent to applicant’s disclosure or directed to the state of art is listed on the enclosed PTO-892. The following is a list of the relevant prior art that was cited but not applied: Harvey (US 2021/0383219 A1), Choi (US 2022/0041180 A1), and Garimella (US 10,829,149 B1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAITLIN MCCLEARY whose telephone number is (703)756-1674. The examiner can normally be reached Monday - Friday 10:00 am - 7:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Navid Z Mehdizadeh can be reached at (571) 272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CAITLIN R MCCLEARY/Examiner, Art Unit 3669
