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, 4-5, and 8 were previously pending. Claims 1 and 5 have been amended. No claims have been cancelled or newly added. Accordingly, claims 1, 4-5, and 8 remain pending and have been examined in this application.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/3/2026 has been entered.
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.
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: “sensed information unit” in claims 1, 4-5, and 8.
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: “sensed information unit” in claims 1, 4-5, and 8 uses a generic placeholder “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:
Sensed information unit: [0208] - The sensed information may be of any kind and may be sensed by any type of sensors - such as a visual light camera, an audio sensor, a sensor that may sense infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc. The sensing may include generating samples (for example, pixel, audio signals) that represent the signal that was transmitted, or otherwise reach the sensor.
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 § 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, 4-5, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost ‘150 (US 2024/0101150 A1) in view of Pronovost ‘458 (US 2024/0217548 A1).
Regarding claim 1, Pronovost ‘150 discloses a method of providing selective learning by prediction for driving (see at least Fig. 2, abstract, [0045, 0047-0048, 0051-0052]), the method comprising: obtaining, by a machine learning process using an artificial neural network trained across road elements (see at least Fig. 2, [0016, 0020, 0055]), a first set of tokens with respect to a first element captured in a sensed information unit in an environment of a vehicle, the first set of tokens representing respective attributes characterizing the first element (see at least Fig. 2, [0020, 0038-0039, 0044-0048, 0054-0055] – feature vectors generated to represent a current state of the object (e.g., a heading, a speed, etc.) and/or a behavior of the object over time (e.g., a change in yaw, speed, or acceleration of the object)… the codebook can receive feature vectors and map them to corresponding tokens… sample tokens that represent potential interactions between the object and the vehicle… sensor data); obtaining, by the machine learning process, a second set of tokens generated with respect to the vehicle and representing respective attributes characterizing the vehicle (see at least Fig. 2, [0020, 0038-0039, 0044-0048] – feature vectors representing a vehicle… feature vectors represent a current state of the vehicle 102… the codebook can receive feature vectors and map them to corresponding tokens… sample tokens that represent potential interactions between the object and the vehicle); obtaining, by the machine learning process, a scenario indication that is indicative of a scenario faced by the vehicle in the environment (see at least Fig. 2, [0047-0048] – scene data, environmental data); processing, by the machine learning process, the first set of tokens in correspondence with the second set of tokens and with respect to the scenario (see at least [0044-0048] - the Transformer model 204 to determine the token sequence 206 with consideration to historical object state data, scene data, environmental data), the processing comprising: selecting, based on the scenario indication and based on attention scores learned during training, a sub-set of first tokens from the first set of tokens (see at least Fig. 2, Claim 14, [0019-0020, 0044-0049, 0075, 0085, 0093] – Transformer model 204 can sample tokens from the codebook 202 and arrange the tokens in a sequence that represents potential interactions between objects the vehicle, the Transformer model 204 can include one or more self-attention layers that determine scores and arrange the tokens… for example, the token sequence 206 can include subsequent tokens representing an action or state of an object… training the machine learning model (e.g., Transformer model) can include adjusting determinations by one or more self-attention layers to improve how tokens are arranged into a sequence by the one or more self-attention layers… determine the token sequence 206 with consideration to historical object state data, scene data, environmental data, and so on); selecting, based on the scenario indication and based on the attention scores, a second sub-set of second tokens from the second set of tokens (see at least Fig. 2, [0044-0049] – Transformer model 204 can sample tokens from the codebook 202 and arrange the tokens in a sequence that represents potential interactions between objects the vehicle, the Transformer model 204 can include one or more self-attention layers that arrange the tokens… for example, the token sequence 206 can include a first token representing an action by the vehicle… determine the token sequence 206 with consideration to historical object state data, scene data, environmental data, and so on); activating the first sub-set of tokens and the second sub-set of tokens for the scenario (see at least Fig. 2, [0044, 0047-0051] – token sequence 206 having tokens that represent potential interactions between the object and the vehicle… token sequence 206 input into a machine learned model 208); producing, based on the activated first sub-set of tokens and the activated second sub-set of tokens, an image-level representation for the first element with respect to the vehicle (see at least Fig. 2, [0051-0052] – token sequence 206 input into a machine learned model 208 configured to generate output data 210… the output data 210 can include one or more: an object trajectory, a heatmap, or scene data); determining, based on the produced image-level representation, an interaction of the first element with respect to the vehicle in the scenario (see at least Fig. 2, [0047-0052] – interaction between the object and the vehicle… the output data 210 can include a scene 212 which can further include the object trajectory 116); determining, based on the interaction, a driving related output with respect to the vehicle, wherein the driving related output is an instruction to execute an autonomous driving operation; and performing the autonomous driving operation (see at least Figs. 2, 9, [0043, 0142] – causing the vehicle to be controlled in a real-world environment based at least in part on the object trajectory… planning for the possibility that the object may intersect with the vehicle in the future).
Pronovost ‘150 does not appear to explicitly disclose attention scores that indicate relevancy of tokens to the scenario, wherein irrelevant tokens are omitted from processing; wherein the attention scores are scenario specific, such that an attention score of a given token differs from one scenario to another scenario.
Pronovost ‘458, in the same field of endeavor, teaches the following limitations: attention scores that indicate relevancy of objects/agents/vectors to the scenario (see at least [0016, 0061] – object interaction vectors and agent feature vectors… a set of attention scores for indicating how relevant each of the objects are to the agent for determining its next action in the environment… a set of attention scores for each agent in the environment), wherein irrelevant tokens are omitted from processing (see at least [0049-0050] – predict which n objects in the environment are most likely to be relevant for predicting the trajectory of the agent… different objects in the environment are removed (or masked) from the model input); wherein the attention scores are scenario specific, such that an attention score of a given object/agent/vector differs from one scenario to another scenario (see at least [0049-0055] - As noted above, in some examples, the object selection component 202 may update and/or re-determine the subset of relevant objects for an agent at different timesteps during the prediction of the trajectory for the agent. These examples may be advantageous when some objects are relevant and should be attended to by the agent early in the driving scene, but may no longer be relevant and need not be attended to by the agent later in the driving scene.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have incorporated the teachings of Pronovost ‘458 into the invention of Pronovost ‘150 with a reasonable expectation of success. The motivation of doing so is to provide a scalable, fixed-cost ML attention mechanism, utilize attention mechanisms to provide significant reductions in data storage and compute requirements, reducing the potential for latency spikes in crowded environments, and provide more efficient and accurate predictions for agents and objects (Pronovost ‘458 – [0015, 0025]).
Regarding claim 4, Pronovost ‘150 discloses further comprising identifying the identified scenario, by the machine learning process (see at least [0044, 0047-0048] – scene data, environmental data).
Regarding claim 5, all the limitations have been analyzed in view of claim 1, and it has been determined that claim 5 does not teach or define any new limitations beyond those previously recited in claim 1; therefore, claim 5 is also rejected over the same rationale as claim 1.
Regarding claim 8, all the limitations have been analyzed in view of claim 4, and it has been determined that claim 8 does not teach or define any new limitations beyond those previously recited in claim 4; therefore, claim 8 is also rejected over the same rationale as claim 4.
Response to Arguments
Applicant’s arguments, see pages 5-8 filed 9/3/2026, with respect to the prior art rejections regarding the Foster reference have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Pronovost ‘458.
Applicant's arguments, see pages 7-8 filed 9/3/2026, with respect to the prior art rejections regarding the Pronovost reference (now referred to as Pronovost ‘150) have been fully considered but they are not persuasive. Applicant argues that Pronovost ‘150 does not disclose a first set of tokens representing attributes characterizing the first element from which a sub-set can be selected. One token carries the attributes, there is no set of multiple tokens per element from which a sub-set is selected. The examiner respectfully disagrees.
Applicant points to paragraph [0044] of Pronovost ‘150 which recites “As illustrated, a codebook 202 can store tokens that represent object behavior, vehicle behavior, and/or environment features. For example, a first token can represent an object state, a second token can represent a vehicle state, and a third token can represent environment features (e.g., a traffic signals, crosswalk, weather, etc.). The object state can indicate a position, orientation, velocity, acceleration, yaw, etc. of an object. A token may also or instead indicate an action by the object and/or the vehicle (e.g., go straight, turn right, turn left, etc.). In various examples, the codebook 202 can be configured to store a certain number of tokens.” This recitation does not explicitly limit the codebook 202 to a single token representing all attributes of the object, but instead is stating various examples of different types object states that can be represented by corresponding tokens. To support this and further clarify, see paragraph [0020] of Pronovost ‘150 which in part recites “The codebook can receive the feature vectors from the machine learned model and map the feature vectors to corresponding tokens by comparing descriptions (e.g., metadata indicating an identifier, an index number, etc.) associated with each feature vector and token. In various examples, the feature vectors may be generated to represent a current state of the object (e.g., a heading, a speed, etc.) and/or a behavior of the object over time (e.g., a change in yaw, speed, or acceleration of the object). In some examples, the machine learned model determines additional feature vectors to represent other objects and/or features of the environment.” In Pronovost ‘150 there are one or more feature vectors corresponding to the object, and each feature vector for the object is mapped to a corresponding token.
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: Pronovost (US 2024/0104934 A1) and Ye (US 2024/0152734 A1).
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
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