DETAILED ACTION
This action is responsive to the following communications: Response to Restriction Requirement filed on July 2, 2026. All references to this application refer to the U.S. Patent Application Publication No. 2023/0145208 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this case. Claims 1, 4, 5, 7, 8, 12, 13, 15, 18, and 20 were amended. Claims 21-26 were cancelled. Claims 1, 8, and 15 are the independent claims. Claims 1-20 are rejected.
Priority
Applicants properly claim the benefit of U.S. Provisional Patent Application No. 63/276,660, filed on November 7, 2021.
Election/Restrictions
Applicants’ election without traverse of Group I in the reply filed on July 2, 2026, is acknowledged.
Drawings
The drawings are objected to because of the following informalities:
In Fig. 8, there are reference elements 1318(1), 1318(2), and 1318(N), which do not appear in the written description.
This can be corrected in one of two ways: either filing corrected drawings without the reference elements, or by amending the written description to include them, presumably somewhere in paragraphs 0140-0143, which describe the node computing resources 816(N) which are connected to reference elements 1318(N).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the Applicants will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
INFORMATION ON HOW TO EFFECT DRAWING CHANGES
Replacement Drawing Sheets
Drawing changes must be made by presenting replacement sheets which incorporate the desired changes and which comply with 37 CFR 1.84. An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). A replacement sheet must include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the Examiner, Applicants will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless Applicants are notified.
Identifying indicia, if provided, should include the title of the invention, inventor’s name, and application number, or docket number (if any) if an application number has not been assigned to the application. If this information is provided, it must be placed on the front of each sheet and within the top margin.
Annotated Drawing Sheets
A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be submitted or required by the Examiner. The annotated drawing sheet(s) must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change(s) to the drawings.
Timing of Corrections
Applicants are required to submit acceptable corrected drawings within the time period set in the Office action. See 37 CFR 1.85(a). Failure to take corrective action within the set period will result in ABANDONMENT of the application.
If corrected drawings are required in a Notice of Allowability (PTOL-37), the new drawings MUST be filed within the THREE MONTH shortened statutory period set for reply in the “Notice of Allowability.” Extensions of time may NOT be obtained under the provisions of 37 CFR 1.136 for filing the corrected drawings after the mailing of a Notice of Allowability.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0058, the last sentence reads “Examples of such inferences include, but are not necessarily limited to, control signals to cause robot 102 to move, grasp and object, release an object, or perform some other task; and to identify or evaluate concepts perceptible in an image or in video.” This should recite “Examples of such inferences include, but are not necessarily limited to, control signals to cause robot 102 to move, grasp an object, release an object, or perform some other task; and to identify or evaluate concepts perceptible in an image or in video.”
In paragraph 0071, the second sentence is missing a word, reading “In this example 200, privileged information 202 comprises positions and orientations of two objects, but more generally may information obtained from a simulation program that is not similar to what might be available at test or deployment time.” It appears that the missing word should be something like “include” or “comprise.”
Paragraph 0086 includes what appear to be references to papers (“[23],[24]”), but it is unclear what papers are being referenced. These should either be clarified or removed.
As described above, in Fig. 8, there are reference elements 1318(1), 1318(2), and 1318(N), which do not appear in the written description.
This can be corrected in one of two ways: either filing corrected drawings without the reference elements, or by amending the written description to include them, presumably somewhere in paragraphs 0140-0143, which describe the node computing resources 816(N) which are connected to reference elements 1318(N).
Paragraphs 0634 and 0635 appear to be placeholders, reciting “<THIS SECTION WILL BE UPDATED ONCE CLAIMS ARE FINALIZED>” and “<Insert here clauses corresponding to the claims, for EPO practice and spec support>” respectively. These paragraphs should be updated or removed as necessary.
Appropriate corrections are required.
The use of trademarks has been noted in this application. The term should be accompanied by the generic terminology, if appropriate; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The Specification has not been examined to the extent necessary to determine the presence of improperly marked trademarks. The Applicants’ cooperation is requested in correcting any errors of which the Applicants may become aware in the Specification.
Claim Interpretation - 35 USC § 101
Claims 1-20 recite subject matter that is similar to Example 47, claims 1 and 3 of the July 2024 Subject Matter Eligibility Examples.
Specifically, the claims do not recite any abstract ideas, such as a mathematical concept, mental process, or a method of organizing human activity, such as a fundamental economic concept or managing interactions between people. See MPEP 2106.04(a)(2). While machine learning models may be trained using mathematics, there is no mathematical concept recited in the claim. Because the claims do not recite a judicial exception (Step 2A, Prong One: NO), it cannot be directed to one (Step 2A: NO). The claims are eligible.
Examiner’s Note
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7, 8, 10, 11, 13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0105624 A1, filed by Kalakrishnan et al., as a U.S. National Stage Entry on July 12, 20211, and published on April 7, 2022 (hereinafter Kalakrishnan), in view of U.S. Patent Application Publication No. 2019/0228495 A1, filed by Tremblay et al., on January 23, 2019 , and published on July 25, 2019 (hereinafter Tremblay).
With respect to independent claim 1, Kalakrishnan discloses a system, comprising:
At least one processor; and at least one memory comprising instructions that, in response to execution by the at least one processor, cause the system to at least; Kalakrishnan discloses a system comprising a processor, memory, stored instructions, an autonomous agent (e.g., robot) (see Kalakrishnan, Figs. 1, 10, and 11; see also, Kalakrishnan, paragraphs 0057-0061 [describing the architecture of Fig. 1, including a robot comprising a vision component (e.g., camera), grasping effector, and data to train multiple different machine learning models (ML models)], 0129-0131 [describing the architecture of Fig. 10, including processor and memory], and 0133-0138 [describing the architecture of Fig. 11, a computing device for performing aspects of the invention]).
Obtain an inference using a second machine learning model that was trained to infer a concept in part using a first machine learning model, Kalakrishnan discloses using a second ML model, trained, in part, by a first ML model, to infer a concept (see Kalakrishnan, Figs. 2A-B; see also, Kalakrishnan, paragraphs 0009-0010 [describing meta-learning models trained using expert (e.g., human) demonstrations of a task, which generates task embeddings which are then used to train a task model to perform the desired task] and 0065-0069 [describing the meta-learning models of Figs. 2A-B, which uses state data (such as pose or object features), including simulated vision data that capture data of a simulated environment and robot state data (effector-post, angle, velocity, other position information of the robot) used to train a second model that obtains an inference as to the actions to take to successfully complete the task; 2B includes context embedding in addition to demonstration embeddings and trial embeddings of the task (or attempts to complete the task)]).
The first machine learning model to have been trained to infer the concept based, at least in part, on first information obtained from a simulation of a virtual environment and input indicative of the concept, Kalakrishnan discloses the first ML model being trained, in part, on information obtained from a simulation of a virtual environment (see Kalakrishnan, paragraphs 0065-0069, described supra).
The second machine learning model to have been trained to infer the concept based, at least in part, on second information generated by the simulation…; Kalakrishnan discloses the second ML model trained to infer the concept based on second information generated by the simulation and information generated from the first ML model (see Kalakrishnan, paragraphs 0065-0069, described supra).
Kalakrishnan does not expressly disclose training the second ML model using labels generated using the first machine learning model.
However, Tremblay teaches the first ML model generates labels as output from the simulated environment that are used to infer the concept for the robot task (see Tremblay, Figs. 3A-B; Tremblay, paragraphs 0016 [data representative of the task is input and used to infer a set or precepts about the task, which are then used as inputs to a second ML model to infer a set of actions for completing the task], 0023 [robot processes the data to attempt to determine or define the task and develop a plan to perform the task], 0029 [system includes three models, each being a ML model that performs a different aspect of the task planning and performance], 0053 [describing how a deep learning network (DNN) to generate labels which can be provided as input to further ML models], 0054 [during training, data flows through the DNN via forward propagation until a prediction label corresponding to the input is produced], and 0056 [predictions, labels, and other outputs can be produced from the input processed by the DNN]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Kalakrishnan and Tremblay before him before the effective filing date of the claimed invention, to modify the system of Kalakrishnan to incorporate the first ML model producing labels which are then used as inputs to a forward ML model as taught by Tremblay. One would have been motivated to make such a combination because this decreases the time and costs of training autonomous agents (e.g., robots) to perform tasks, as taught by Tremblay (see Tremblay, paragraph 0002 [“Robotic devices are being utilized to perform an increasing number and variety of tasks. Using conventional approaches, a programmer must spend a significant amount of time programming and testing a robot, or other automated device or object, to perform a physical task. This comes at a high cost, both in programming cost and robot downtime, that makes the use of robotic devices financially prohibitive for many potential users. Further, the expense requires significant usage time to recoup the costs, which limits the ability to make changes or add new tasks to be performed.”]).
With respect to dependent claim 2, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Kalakrishnan further teaches the system wherein the first information comprises at least one of object position, object pose, object movement, object appearance, or bounding boxes.
Kalakrishnan further teaches the first information comprising at least object position, appearance, pose, or movement (see Kalakrishnan, paragraphs 0065-0069, described supra, claim 1).
With respect to dependent claim 3, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Kalakrishnan further teaches the system wherein the second information comprises at least one of two-dimensional image data, three-dimensional image data, or point cloud data.
Kalakrishnan further teaches the second information comprises at least 2D or 3D image data (see Kalakrishnan, paragraph 0058, described supra, claim 1).
With respect to dependent claim 4, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Kalakrishnan further teaches the system wherein obtaining the inference using the second machine learning model is to comprise using the second machine learning model to infer the concept based, at least in part, on information obtained from a non-virtual environment.
Kalakrishnan further teaches using information obtained from real-world environments to obtain the inference (see Kalakrishnan, paragraphs 0061-0062 [training of the second ML model is performed using reinforcement learning using information from earlier trials/attempts by the robot itself to perform the task]).
With respect to dependent claim 5, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Kalakrishnan further teaches the system wherein the at least one memory comprises further instructions that, in response to execution by the at least one processor, cause the system to at least: generate one or more control signals to cause a device to move.
Kalakrishnan further teaches generating control signals to cause the robot to move (see Kalakrishnan, paragraphs 0057-0061, 0129-0131, and 0133-0138, described supra, claim 1).
With respect to dependent claim 7, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Tremblay further teaches the system wherein the at least one memory comprises further instructions that, in response to execution by the at least one processor, cause the system to at least: use a third machine learning model to perform a task based at least in part on a determination of compliance with the concept, wherein obtaining the inference using the second machine learning model comprises generating the determination.
Tremblay further teaches using a third ML model to perform a task based on determination of compliance with the concept determined by the second ML model (see Tremblay, Figs. 2A-D; see also, Tremblay, paragraph 0067 [a prediction accuracy metric is determined to report on the overall success of the system in terms of success in completion of the task]; see also, Tremblay, paragraphs 0016, 0023, 0029, 0053, 0054, and 0056, described supra, claim 1).
Independent claim 8, and its respective dependent claim 13, recite a method performed by the system of independent claim 1, and its respective dependent claim 7. Accordingly, independent claim 8, and its respective dependent claim 13, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claim 7, which are incorporated herein.
Independent claim 15, and its respective dependent claim 20, recite a non-transitory computer-readable medium comprising instructions that, when performed by at least one processor of a computing device, cause the computing device to at least perform as the system of independent claim 1, and its respective dependent claim 7. Accordingly, independent claim 15, and its respective dependent claim 20, are rejected under the same rationales used to reject independent claim 1, and its respective dependent claim 7, which are incorporated herein.
With respect to dependent claim 10, Kalakrishnan, as modified by Tremblay, teaches the method of claim 8, as described above.
Kalakrishnan further teaches the method wherein the simulation of the virtual environment comprises simulation of a robot interacting with the virtual environment.
Kalakrishnan further teaches the robot can be a simulated robot interacting within a simulated environment (see Kalakrishnan, paragraph 0065, described supra, claim 1).
With respect to dependent claim 11, Kalakrishnan, as modified by Tremblay, teaches the method of claim 8, as described above.
Kalakrishnan further teaches the method, further comprising: performing, by a robot, a task in compliance with the concept indicated by the input demonstrative of the concept.
Kalakrishnan further teaches performing the task in compliance with the determined concept by a robot (see Kalakrishnan, paragraphs 0065-0069, described supra, claim 1).
Dependent claims 17 and 18 recite a non-transitory computer-readable medium comprising instructions that, when performed by at least one processor of a computing device, cause the computing device to at least perform the method of dependent claims 10 and 11. Accordingly, dependent claims 17 and 18 are rejected under the same rationales used to reject dependent claims 10 and 11, which are incorporated herein.
With respect to dependent claim 19, Kalakrishnan, as modified by Tremblay, teaches the non-transitory computer-readable medium of claim 15, as described above.
Kalakrishnan further teaches the method wherein the first information has lower dimensionality than the second information.
Kalakrishnan further teaches the first information having low-dimensionality (pose, angle, position, orientation, etc.), and the second information having high-dimensionality (2D/3D images) (see Kalakrishnan, paragraphs 0058 and 0065-0069, described supra, claim 1).
Claims 6, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kalakrishnan, in view of Tremblay, further in view of Non-Patent Literature reference entitled “Elaborating on Learned Demonstrations with Temporal Logic Specifications,” by Innes et., available at arXiv:2002.00784v2, published on May 22, 2020 (hereinafter Innes).
With respect to dependent claim 6, Kalakrishnan, as modified by Tremblay, teaches the system of claim 1, as described above.
Although Kalakrishnan teaches using user feedback to confirm ML model outputs (see Kalakrishnan, paragraphs 0027-0028 [using user feedback to determine reward functions and improve agent policy], Kalakrishnan and Tremblay fail to further teach the system wherein the input comprises one or more queries of a user, the queries of the user associated with the concept.
However, Innes teaches the input comprising user queries associated with the concept (see Innes, Section I [describing how the learning from demonstration (LfD) techniques is augmented based on additional user input to clarify or modify information associated with the concept, particularly for tasks that are either difficult to demonstrate or have underlying complex specifications], Section III.A [describing one-shot experiments with four types of tasks (avoid, patrol, steady, and slow), in which an initial concept is determined and then updated based on user queries], and Section III.C [describing a particular set of tasks with a robot for pouring and reaching in which user updates and elaborations are made after the initial demonstrations are performed]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Kalakrishnan, Tremblay, and Innes before him before the effective filing date of the claimed invention, to modify the system of Kalakrishnan, as modified by Tremblay, to incorporate the input comprising user queries as taught by Innes. One would have been motivated to make such a combination because this improves agent training of highly complex tasks, particularly those with temporal aspects, as taught by Innes (see Innes, Section 1 [“We incrementally build up task-learning by first learning a demonstration, then providing additional specifications later. In this sense, our work is close to the methodology of Interactive Task Learning (ITL). In such work, the goal is not just for the
system to learn a given task, but to incrementally build an understanding of the task itself with help from the user.”]).
With respect to dependent claim 12, Kalakrishnan, as modified by Tremblay, teaches the method of claim 8, as described above.
Kalakrishnan further teaches the method wherein the inference is to be generated by the second machine learning model using third information obtained from an environment that is external to the virtual environment…
Kalakrishnan further teaches the second ML model generating the inference based on third information obtained from an environment that is external to the virtual environment (see Kalakrishnan, paragraphs 0061-0062, described supra, claim 4).
Although Tremblay teaches the determination of a prediction accuracy metric (see Tremblay, paragraphs 0067, described supra, claim 7), Kalakrishnan and Tremblay fail to further teach the inference comprises at least one value indicating a degree to which the concept appears in the third information.
However, Innes teaches determining both imitation loss and constraint loss, which indicates a degrees to which the concept appears in the third information (see Innes, Section II.A [defining imitation loss, which gives the distance between the demonstration and the learned task trajectories] and Section II.B [defining the constraint loss, which measures how close the task is to being satisfied]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Kalakrishnan, Tremblay, and Innes before him before the effective filing date of the claimed invention, to modify the method of Kalakrishnan, as modified by Tremblay, to incorporate a value indicating a degree to which the concept appears in the third information as taught by Innes. One would have been motivated to make such a combination because this improves agent training of highly complex tasks, particularly those with temporal aspects, as taught by Innes (see Innes, Section 1, described supra, claim 6).
With respect to dependent claim 14, Kalakrishnan, as modified by Tremblay, teaches the method of claim 8, as described above.
Although Kalakrishnan teaches using user feedback to confirm ML model outputs (see Kalakrishnan, paragraphs 0027-0028, described supra, claim 6), Kalakrishnan and Tremblay fail to further teach the method wherein the input demonstrative of the concept is obtained based, at least in part, on queries of a user of the virtual environment.
However, Innes teaches the input and demonstrations comprising user queries associated with the concept (see Innes, Section I, Section III.A, and Section III.C, described supra, claim 6).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Kalakrishnan, Tremblay, and Innes before him before the effective filing date of the claimed invention, to modify the method of Kalakrishnan, as modified by Tremblay, to incorporate the input comprising user queries as taught by Innes. One would have been motivated to make such a combination because this improves agent training of highly complex tasks, particularly those with temporal aspects, as taught by Innes (see Innes, Section 1, described supra, claim 6).
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kalakrishnan, in view of Tremblay, further in view of Non-Patent Literature reference entitled “Learning by cheating,” by Chen et., available at arXiv:1912.12294v1, published on December 27, 2019 (hereinafter Chen).
With respect to dependent claim 9, Kalakrishnan, as modified by Tremblay, teaches the method of claim 8, as described above.
Kalakrishnan and Tremblay fail to further teach the method wherein the first information comprises privileged simulation information and the second information does not comprise privileged simulation information.
However, Chen teaches a multi-model system for operating a robot (autonomous vehicle) that employs a first privileged model which acts as a teacher to a second student model, which does not have access to the privilege information (see Chen, section 1 [describing the process of first training an agent that has access to privileged information from the simulation, and that agent is then used to train a second agent that does not have access to the privileged information], Section 2 [a privileged agent is first taught with access to a map and ground truth information of the environment only available to the privileged agent] and Section 2.1 [describing how the privileged agent uses the privileged information]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Kalakrishnan, Tremblay, and Chen before him before the effective filing date of the claimed invention, to modify the system of Kalakrishnan, as modified by Tremblay, to incorporate the first ML model using privileged information and a second ML model that does not have access to privileged information as taught by Chen. One would have been motivated to make such a combination because this improves complex autonomous agent training of highly complex tasks, as taught by Chen (see Chen, Section 1 [“Concretely, the decomposition provides three advantages. First, the privileged agent operates on a
compact intermediate representation of the environment, and can thus learn faster and generalize
better [25, 26]. In particular, the representation we use (a bird’s-eye view) enables simple and effective data augmentation that facilitates generalization. Second, the trained privileged agent can provide much stronger supervision than the original expert trajectories. It can be queried from any state of the environment, not only states that were visited in the original trajectories. This enables automatic DAgger-like training in which supervision from the privileged agent is gathered adaptively via online rollouts of the sensorimotor agent [17, 21, 24]. It turns passive expert trajectories into an online agent that can provide adaptive on-policy supervision. The third advantage is that the privileged agent produced in the first stage is a “white box”, in the sense that its internal state can be examined at will. In particular, if the privileged agent is trained via conditional imitation learning [6], it can provide an action for each possible command (e.g., “turn left”, “turn right”) in the second stage, all at once, in any state of the environment. Thus all conditional branches of the privileged agent can train all branches of the sensorimotor agent in parallel. In every state visited during training, the sensorimotor student can in effect ask the privileged teacher “What would you do if you had to turn left here?”, “What would you do if you had to turn right here?”, etc. This is both a powerful form of data augmentation and a high-capacity learning signal.”]).
Dependent claim 16 recites a non-transitory computer-readable medium comprising instructions that, when performed by at least one processor of a computing device, cause the computing device to at least perform the method of dependent claim 9. Accordingly, dependent claim 16 is rejected under the same rationales used to reject dependent claim 9, which are incorporated herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure. See PTO-892.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicants are encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center to authorized users only. Should you have questions about access to the USPTO patent electronic filing system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ERIC J. BYCER/
Primary Examiner
Art Unit 2141
1 Kalakrishnan filed as PCT/US2020/014848 on January 23, 2020, and further claims the benefit of U.S. Provisional Patent Application No. 62/796,036, filed on January 23, 2019.
Prosecution Insights