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 .
This office action is sent in response to Applicant’s communication received on 05/06/2025 for the application number 19199877. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
Status of the claims
Claims 1-20 are presented for examination.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to an abstract idea without significantly more. The claim(s) does/do not include additional
elements that are sufficient to amount to significantly more than the judicial exception because as
explained below.
Claim 1 recites a computer-implemented method for generating a human-interpretable/comprehensible explanation of an autonomous mobile robot (AMR) action, the computer-implemented method comprising:
receiving at least one image from a camera stream associated with the AMR;
generating a visual saliency heatmap using the at least one image and the AMR action;
determining whether or not the AMR action will cause a potential social conflict with at least one human;
responsive to determining that the AMR action will cause a potential social conflict with at least one human, generating an explanation of the AMR action;
rendering the explanation for perception by the at least one human.
Step (a) comprises data gathering. This step merely receives image information from
camera stream associated with the AMR.
Step (b) comprises a mental process. This step can be performed by a human as a person can evaluate an image and an AMR action and identify visually salient portions of the image.
Step (c) comprises a mental process. This step can be performed by a human as a person can evaluate an AMR action and determine whether the action will cause a potential social conflict with another person.
Step (d) comprises a mental process. This step can be performed by a human as a person can evaluate an AMR action and generate an explanation for the action.
Step (e) comprises insignificant extra-solutional activity. This step merely presents the generated explanation for perception by a human.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any
statutory category. See MPEP 2106.03. The claim recites at least method. Thus, the claim is a
process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the
claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim
“recites” a judicial exception when the judicial exception is “set forth” or “described” in
the claim. As discussed above, the broadest reasonable interpretation of steps (b)-(d)
recites a mental process.
Specifically, step (b) can be performed by a human as a person can evaluate an image and an AMR action and identify visually salient portions of the image.
Step (c) can be performed by a human as a person can evaluate an AMR action and determine whether the action will cause a potential social conflict with another person.
Step (d) can be performed by a human as a person can evaluate an AMR action and generate an explanation for the action.
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a
whole integrates the recited judicial exception into a practical application of the exception or
whether the claim is “directed to” the judicial exception. This evaluation is performed by (1)
identifying whether there are any additional elements recited in the claim beyond the judicial
exception, and (2) evaluating those additional elements individually and in combination to
determine whether the claim as a whole integrates the exception into a practical application. See
MPEP 2106.04(d).
The claim recites additional elements including the camera stream associated with the AMR, the at least one image received from the stream, and the AMR. However, these elements are recited at a high level of generality and perform generic computer functions. The use of these elements to obtain information regarding the AMR and present the resulting explanation constitutes a generic computational technique that merely automates the mental processes described above. Such processing does not impose any meaningful limit on the judicial exception. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole
amounts to significantly more than the recited exception i.e., whether any additional element, or
combination of additional elements, adds an inventive concept to the claim. As explained with
respect to Step 2A, Prong Two, the camera stream associated with the AMR, the at least one image received from the stream, and the AMR comprise additional elements that do not contribute to the patentability of the claim as a whole. They constitute at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). At Step 2B, the evaluation of the insignificant extra solutional activity consideration takes into account whether or not the extra-solutional activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As known in the art these elements are well routine and conventional. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solutional activity which do not provide an inventive concept. The claim is not patent eligible.
Claim 2 recites insignificant extra-solutional activity as including the visual saliency heatmap in the explanation merely presents the result of the recited process.
Claim 3 recites a mental process as a human can identify features of an image and use the identified features to generate an explanation of the action.
Claim 4 recites generic implementation of the abstract idea using a vision language model.
Claim 5 recites insignificant extra-solutional activity as displaying the visual saliency heatmap and natural language explanation merely outputs the results of the recited process.
Claim 6 recites insignificant extra-solutional activity as displaying the heatmap and outputting synthesized speech merely communicates the results of the process.
Claim 7 recites a mental process as a human can describe an AMR action in the context of an observed image.
Claim 8 recites generic implementation of the abstract idea using a Bootstrap Language-Image pretraining model to generate the caption.
Claim 9 recites generic implementation of the abstract idea using Gradient-weighted class activation mapping and a residual network model to identify image regions.
Claim 10 recites generic implementation of the abstract idea using a large language model to generate the natural language expression.
Claim 11 recites a mental process as a human can evaluate whether an action is sufficiently likely to cause discomfort.
Claim 12 recites insignificant extra-solutional activity as projecting the proposed path merely communicates the result of the process.
Claim 13 recites a mental process as a human can determine whether an action will cause discomfort to another person.
Claim 14 recites a mental process as a human can determine whether an alternative action will cause discomfort to another person.
Claim 15 recites a mental process as a human can determine a person’s proximity to a planned path and whether the person will have a line of sight to the AMR.
Claim 16 recites a mental process as a human can determine a person’s proximity to a planned path and whether the person will have a line of sight to the AMR.
Claim 17 recites a mental process as a human can determine whether another person will be able to hear the AMR along a planned path.
Claim 18 recites a mental process as a human can determine whether another person’s activity will be interrupted by a planned path.
Claim 19 recites a mental process as a human can evaluate the latency and content of an explanation and select an explanation to increase its utility.
Claim 20 is analogous to claim 1 because it recites substantially the same limitations. It is therefore rejected for the same reasons.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2-6, 13-14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Binotto et al. (US 20180117769 A1) in view of Burachas et al. (US 20190370587 A1).
Regarding claim 1, Binotto teaches receiving at least one image from a camera stream associated with the AMR (Para 0025, the user’s emotional state is analyzed from “retrieved audio-visual streams” including “video”, where in para 0037, the robot system may interface with the video); determining whether or not the AMR action will cause a potential social conflict with at least one human (Para 0029, when the robot performs a loud cleaning task while the user listens to music, the mood of the user may change to be aggravated” and the robot changes tasks so “the user is not interrupted”, and para 0030, where a pending robot task is determined to be “not optimal at this point in time” because of the social consequences with the human); responsive to determining that the AMR action will cause a potential social conflict with at least one human, generating an explanation of the AMR action (Para 0032, the robot “generates an explanation detailing why the robot is performing the task” while considering the user’s emotional state); rendering the explanation for perception by the at least one human (Para 0034, the explanation may be provided in “audio, visual etc.” form).
Binotto does not teach generating a visual saliency heatmap using the at least one image and the AMR action.
However, Burachas teaches generating a visual saliency heatmap using the at least one image and the AMR action (Para 0042, machine learning may analyze input data and identify an “action to be performed” and Para 0060, the system transform an attention mask into “a visual heatmap” identifying image regions that “formed a basis for the result, thus teaching generating a visual heatmap using an image as well as an action).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Burachas in order to provide a visual indication of the portions of an image contributing to the action (Para 0061).
Regarding claim 2, Binotto does not teach wherein the explanation includes the visual saliency heatmap.
However, Burachas teaches wherein the explanation includes the visual saliency heatmap (Para 0060, the system outputs the result with an updated image that “includes the heatmap” and provides “a visual explanation of the result”).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Burachas in order to provide a more comprehensive explanation by including further visual information (Para 0060).
Regarding claim 3, Binotto does not teach extracting features from the at least one image received; generating a natural language explanation from at least one of (i) the features extracted, and/or (ii) the visual saliency heatmap wherein the explanation includes both (i) the visual saliency heatmap and (ii) the natural language explanation.
However, Burachas teaches extracting features from the at least one image received (Para 0070, the image classification model is configured to “encode image data 33 into feature maps 113”); generating a natural language explanation from at least one of (i) the features extracted, and/or (ii) the visual saliency heatmap (Para 0073-0075, the feature maps are represented as “feature vector[s]” and used by the model to “generate … answer 131”), wherein the explanation includes both (i) the visual saliency heatmap and (ii) the natural language explanation (Para 0078, the system outputs the heatmap containing updated image “along with answer 121”, wherein that answer is a natural language explanation).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Burachas in order to provide a more comprehensive explanation by including further visual information (Para 0060).
Regarding claim 4, Binotto modified Burachas, as above in claim 1, does not teach wherein the natural language explanation is generated from at least one of (i) the features extracted, and/or (ii) the visual saliency heatmap, by generating from the features extracted, a caption using a vision language model (VLM); and generating the natural language explanation from the caption and the visual saliency heatmap ( Para 0073-0075, the feature maps are represented as “feature vector[s]” and used by the model to “generate … answer 131”, Burachas )
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Burachas in order to provide a more comprehensive explanation by including further visual information (Para 0060).
Regarding claim 5, Binotto modified by Burachas as above in claim 4, teach wherein the act of rendering the explanation for perception by at least one human includes displaying both (1) the visual saliency heatmap ( Para 0026, 0060, the system transform an attention mask into “a visual heatmap” identifying image regions that “formed a basis for the result, thus teaching generating a visual heatmap using an image as well as an action, Burachas) and (2) the natural language explanation ( Para 0032, Fig 4, Binotto, Fig 6, Burachas)
Regarding claim 6, Binotto teach synthesizing speech from the natural language explanation, and outputting, via a speaker, the speech synthesized ( Para 0032, output the explanation, Binnoto) wherein the act of rendering the explanation for perception by at least one human includes (1) displaying the visual saliency heatmap ( (Para 0060, the heatmap is overlaid on the image to provide “a visual explanation of the result”, Burachas)
Regarding claim 11, Binotto does not teach wherein the act of determining whether or not a potential social conflict exists includes determining whether or not the AMR action is more probable than a predetermined threshold to cause human discomfort ( Para 0032, Fig 4. the “U score” for each of the available explanations (i.e., Exp01, . . . , Exp04) can be modeled as a linear function “U score”=α*(Mood level)+β *(Patience level)+δ *(Excitement level)+ε*(Stress level), where the weights α, β, δ and ε may vary differently over time for each of the explanations. The emotional levels are obtained from monitoring the user. FIG. 4 illustrate the final “U scores”. Note that a similar approach could be used for “E scores”, where instead of emotional data, the robot would consider data related to the robot and environment. Historical data about previous operations may also be considered. In this same figure, the “Final Score” is basically the sum of the corresponding “E score” and “U score”; and the explanations are ranked from lowest to higher “Final Score” (lowest is better, in the example).
Regarding claim 13, Binotto teaches wherein the potential social conflict is a potential discomfort caused to the at least one human by the AMR action (Para 0029, when the robot performs a loud cleaning task while the user listens to music, the mood of the user may change to be aggravated” and the robot changes tasks so “the user is not interrupted”, and para 0030, where a pending robot task is determined to be “not optimal at this point in time” because of the social consequences with the human).
Regarding claim 14, Binotto does not teach wherein the potential social conflict is a potential discomfort caused to the at least one human by an alternative to the AMR action ( Para 0032, Fig.4, the “U score” for each of the available explanations (i.e., Exp01, . . . , Exp04) can be modeled as a linear function “U score”=α*(Mood level)+β *(Patience level)+δ *(Excitement level)+ε*(Stress level), where the weights α, β, δ and ε may vary differently over time for each of the explanations. The emotional levels are obtained from monitoring the user. FIG. 4 illustrate the final “U scores”. Note that a similar approach could be used for “E scores”, where instead of emotional data, the robot would consider data related to the robot and environment. Historical data about previous operations may also be considered. In this same figure, the “Final Score” is basically the sum of the corresponding “E score” and “U score”; and the explanations are ranked from lowest to higher “Final Score” (lowest is better, in the example)., Fig 4)
Claim 20 is analogous to claim 1 in that it recites substantially the same limitations. It is therefore rejected for the same reasons.
Claims 7, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Ref. A (Li et al., “Visual Analytics for Efficient Image Exploration and User-Guided Image captioning”, arXiv:2311.01016v1, Nov. 2, 2023).
Regarding claim 7, Binotto does not teach wherein the caption is a contextual caption describing the AMR action in the context of the at least one image.
However, Ref. A teaches wherein the caption is a contextual caption describing the AMR action in the context of the at least one image (Sec. 3, Fig. 2, BLIP generates a caption based upon encoded image patches and a textual starting prompt. Sec. 5.3 further teaches customizing that prompt toward the particular subject matter to be described in the resulting caption, while the image-patch embeddings provide the visual context).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Ref. A in order to allow the caption to more specifically describe the robot action in view of the surrounding environment (Sec. 5.3).
Regarding claim 8, Binotto does not teach wherein the caption is generated using Bootstrapped Language Image Pretraining (BLIP).
However, Ref. A teaches wherein the caption is generated using Bootstrapped Language Image Pretraining (BLIP) (Sec. 3, Fig. 3, Ref. A shows “Bootstrapping Language Image Pre-Training (BLIP)” and explains BLIP’s caption generation ability).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Ref. A in order to use an established pretrained language-image model and avoid needing to develop a separate caption generating model (Sec. 3).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1), Burachas (US 20190370587 A1), and further in view of Ref. B (US 20200117951 A1).
Regarding claim 9, Binotto does not teach wherein the visual saliency heatmap is generated using a Gradient-weighted Class Activation Mapping with a Residual Network neural network model to highlight image areas that contributed most to the AMR action.
However, Ref. B teaches wherein the visual saliency heatmap is generated using a Gradient-weighted Class Activation Mapping with a Residual Network neural network model to highlight image areas that contributed most to the AMR action (Para 0040, Grad-CAM may be used to produce “visual explanation maps” that help understand the decision-making process of a neural network, and Para 0041, Grad-CAM “produces a coarse heat map” and “highlight salient image parts”. Additionally, see para 0050, “The ResNet-101 was adopted” as the feature extractor and para 0051, the trained ResNet-101 is used “to generate class-specific attention maps”. Thus, teaching a Gradient -weighted Class Activation Mapping (Grad-CAM) with a residual neural network highlighting image areas).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Ref. B in order to more specifically highlight image regions contributing to the robot action, and improve interpretability of the explanation (Para 0040-0041).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Wells et al. (US 20240253220 A1).
Regarding claim 10, Binotto does not teach wherein the act of generating a natural language expression is performed by a large language model (LLM) external to the AMR.
However, Wells teaches wherein the act of generating a natural language expression is performed by a large language model (LLM) external to the AMR (Para 0110, the robot system generates a natural language query, para 0116, where the robot system provides the query to a large language model (LLM) and para 0117, the robot system receives the outbound verbalization from the LLM. See also para 0105, “an LLM module may be stored and executed outside of a robot” and accessed by the robot system. Thus, teaching using an external LLM to generate natural language output).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Ref. B in order to allow an LLM to generate natural language output, without requiring the LLM to be stored and hosted locally on the robot (Para 0105).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Sun et al. (US 20140316570 A1).
Regarding claim 12, Binotto does not teach wherein the explanation of the AMR action is a proposed path of the AMR, and wherein the act of rendering the explanation for perception by at least one human includes projecting the proposed path of the AMR.
However, Sun teaches wherein the explanation of the AMR action is a proposed path of the AMR (Para 0046, “The long-term intention in the experimental system was both the goal position and the global path the robot decides to take”, teaching communicating the robot’s intended path), and wherein the act of rendering the explanation for perception by at least one human includes projecting the proposed path of the AMR (“Para 0050, “the mid-term intention (intended trajectory) can be projected and superimposed on the real environment” and “the relationship between the planned path and the real-world is obvious”. Sun therefore teaches communicating the robot’s intended action to a human by presenting the planned path the robot intends to follow, such that the projected path functions as an explanation of what action the robot will perform).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Sun in order to allow a human to more readily understand the robot’s proposed action (Para 0050).
Claims 15, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Pinter et al. (US 20150088310 A1).
Regarding claim 15, Binotto does not teach wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be within a predetermined distance of a planned path of the AMR.
However, Pinter teaches wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be within a predetermined distance of a planned path of the AMR (Para 0048, Pinter describes a lockout zone and comfort zone around each detected human, and para 0049, the robot may “create or modify any path to avoid the lockout zone”. See also para 0050, the human lockout zone is defined using predetermined distances including 12, 18, or 36 inch radii).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Pinter in order to reduce human discomfort by maintaining a socially acceptable separation (Para 0050).
Regarding claim 16, Binotto does not teach wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be within a predetermined distance of a planned path of the AMR, and have a line-of-sight of the AMR in the planned path.
However, Pinter teaches wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be within a predetermined distance of a planned path of the AMR (Para 0048-0050, Pinter determines and avoids predetermined lockout zones, and para 0049, the robot may “create or modify any path to avoid the lockout zone”), and have a line-of-sight of the AMR in the planned path (Para 0059, the robot determines “which direction the humans are facing” as well as their proximity. Para 0062, the robot determines its planned path and entry angle to increase visibility to approaching humans. Teaching determining both the humans proximity and line of sight of the planned path).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Pinter in order to make the robot feel more predictable and lower human discomfort by allowing more visibility (Para 0062).
Regarding claim 18, Binotto does not teach wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will have an activity interrupted by a planned path of the AMR.
However, Pinter teaches wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will have an activity interrupted by a planned path of the AMR (Para 0058, the robot detects nearby humans engaged in conversation and establishes a conversation zone. Para 0059, the robot plans its navigational path around the conversational group to “avoid interrupting human interaction”. Teaching determining whether the planned robot path would interfere with ongoing human activity).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Pinter in order to reduce discomfort caused by interruptions to human behavior and allow the robot to avoid causing discomfort (Para 0059).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Agarwal et al. (“Sound Matters: Auditory Detectability of Mobile Robots”, arXiv:2404.06807, Apr. 10, 2024).
Regarding claim 17, Binotto does not teach wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be able to hear the AMR as it navigates a planned path.
However, Agarwal teaches wherein the act of determining whether or not the AMR action will cause a potential social conflict with at least one human includes determining whether or not at least one human will be able to hear the AMR as it navigates a planned path (Abstract, Agarwal measures “auditory detection distances” for a wheeled Turtlebot and another robot that emit constant sounds “when moving”. Sec. III-D, Pg. 4, the detection task “determined the distance at which listeners reliable detected a robot sound in background noise”. Thus, teaching determining based on distance and environmental conditions, whether a human will be able to hear a moving mobile robot).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Agarwal in order to improve human awareness of an approaching robot by determining whether a human will be able to hear a robot on its path (Pg. 4).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Binotto (US 20180117769 A1) and Burachas (US 20190370587 A1) as above, and further in view of Ref. C (“Reasoning about When to Provide Explanation for Human-involved Self-Adaptive Systems”, 2020 IEEE ACSOS, Aug. 2020, pp. 195-204).
Regarding claim 19, Binotto teaches wherein the act of generating the explanation of the AMR action includes increasing or maximizing the utility of the explanation (Para 0032, the robot generates the explanation to “maximize/minimize a given objective function” to determine the best explanation and ranks candidate explanations according to their scores).
Binotto does not teach wherein a utility of the explanation is a function of both (1) a latency needed to generate the explanation, and (2) content of the explanation.
However, Ref. C teaches wherein a utility of the explanation is a function of both (1) a latency needed to generate the explanation, and (2) content of the explanation (Sec. V, explains that increased “explanation latency” reduces system utility. Sec. VI, pg. 203, teaches assigning latency based on “the complexity of information in the explanation content” and suggests “tailoring the optimal amount of information” in an explanation “to maximize overall utility”).
It would have been obvious to one of ordinary skill in the art to modify Binotto before the effective filing date to incorporate the teachings of Ref. C in order to improve overall explanation utility by taking a wider array of variables into account (Pg. 203).
Conclusion
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/MICHAEL A FOSTER JR/Examiner, Art Unit 2654 /Richa Sonifrank/Primary Examiner, Art Unit 2654
