DETAILED ACTION
The Preliminary amendment filed 12/21/2023 has been entered. Claims 1-17 have been amended. Claims 18-20 have been added. Claims 1-20 are pending and have been examined.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claim 19 is objected to because of the following informalities: Claim 19 recites “The method as claimed in claim 18, further comprising an intralogistics system…”, which seems to combine multiple statutory (i.e., a method and a system). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: “component for” in claims 17 and 19.
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.
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 non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Here, under step 1 of the Alice analysis, method claims 1-14 and 18-20 are directed to a series of steps, computer program claim 15, computer-readable medium claim 16 are directed to a program executed by a processor, and system claim 17 directed to a plurality of components. Thus the claims are directed to a process, manufacture, and machine, respectively.
Under step 2A Prong One of the analysis, the claimed invention is directed to an abstract idea without significantly more. The claims recite determining a cause of fault, including provisioning, providing and computing steps.
The limitations of provisioning, providing and computing, are a process that, under its broadest reasonable interpretation, covers organizing human activity concepts, but for the recitation of generic computer components.
Specifically, the claim elements recite provisioning (providing) a plurality of reference anomaly signal sequences relative to respectively associated normal operation signal sequences from signal sequences of a plurality of components of the intralogistics system; wherein the signal sequences describe properties, parameters, and/or pieces of condition information of the respective component; and wherein the reference anomaly signal sequences comprise known anomaly signal sequences from the intralogistics system and/or from at least one other intralogistics system; computing a graph model of the intralogistics system on the basis of the plurality of the provisioned reference anomaly signal sequences; wherein the graph model describes a technical interaction of the components of the intralogistics system with the help of the properties, parameters and/or pieces of condition information; provisioning (providing) an operative anomaly signal sequence relative to an associated normal operation signal sequence from signal sequences of a plurality of components of the intralogistics system; and computing, on the basis of the computed graph model, a piece of operative anomaly cause information relating to the operative anomaly signal sequence of the intralogistics system.
That is, other than reciting a processor and a component for processing orders, the claim limitations merely cover commercial interactions, including business relations, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include a processor and a component for processing orders. The processor and a component for processing orders in the steps is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of a processor and a component for processing orders amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
None of the dependent claims recite additional limitations that are sufficient to amount to significantly more than the abstract idea. Claims 2-5 recite additional provisioning and computing steps, and further describe the computing of the graph model. Claims 6-9 further describe provisioning of the reference anomaly signal sequences or of the operative anomaly signal sequence, and provisioning of the piece of reference anomaly. Claims 10-14 further describes the graph mode and the provisioning of the reference anomaly signal sequences, and recite additional provisioning, computing, generating steps. Similarly, dependent claim 20 recites additional details that further restrict/define the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, a processor and a component for processing orders.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn’t any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed on page 4 of the specification, “components for processing data, for example software components, control devices, in particular warehouse management system (WMS), warehouse control system (WCS), and suchlike,” while page 12 recites “Fig. 1 shows an intralogistics system 10, which can be configured, for example, as a distribution center for different articles. The intralogistics system 10 shown comprises: a rack system 18 for storing the articles, workstations 26 for processing orders that were previously detected electronically, a storage and/or retrieval system which connects the rack system 18 and the workstations 26 in terms of conveyor technology. The rack system 18 has a plurality of storage racks (components for storing articles) installed in a stationary manner. The storage and/or retrieval system comprises one or multiple storage and retrieval units 20, as well as a mobile conveying system 22, for example AGVs and/or AMRs, and/or a stationary conveying system 24 (components for transporting articles) in order to store articles 14 in the storage racks or retrieve them from same, as well as in order to transport the articles 14 between the rack system 18 and the workstations 26.”
As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int’l et al., No. 13-298 (U.S. June 19, 2014).
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Independent claim 15 is directed to “a computer program” which is deemed to merely be software, with no accompanying hardware components or storage device.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lalonde et al (US 20180172450 A1).
As per claim 1, Lalonde et al disclose a computer-implemented method for determining a cause of fault in an intralogistics system (i.e., system 200 for operating one or more warehouses, in accordance with an example embodiment. System 200 includes warehouse management system 210, planning system 110, and robotic device 220, ¶ 0075), comprising the steps:
provisioning a plurality of reference anomaly signal sequences relative to respectively associated normal operation signal sequences from signal sequences of a plurality of components of the intralogistics system (i.e., robotic device(s) 120 can be subject to one or more failure conditions, ¶ 0101); wherein the signal sequences describe properties, parameters, and/or pieces of condition information of the respective component (i.e., Hardware Fault Steering/traction drive fault or Robotic device will halt and other low-level hardware I/O notify human operator. The fault condition operator can power-cycle and manually drive robotic device back onto roadmap. Pallet Detection Failure, Table 1); and
wherein the reference anomaly signal sequences comprise known anomaly signal sequences from the intralogistics system and/or from at least one other intralogistics system (i.e., The component framework can include one or more of: a state machine component, a localization component, a planning component, and a trajectory following component. The state machine component can manage a state of robotic device 120 for vehicle initialization, vehicle commanding and fault handling, ¶ 0090);
computing a graph model of the intralogistics system on the basis of the plurality of the provisioned reference anomaly signal sequences (i.e., FIG. 7 depicts roadmap graph 700 corresponding to prototype graph 600, in accordance with an example embodiment. Robotic devices can travel an environment mapped using roadmap graph 700 using routes or paths, ¶ 0107);
wherein the graph model describes a technical interaction of the components of the intralogistics system with the help of the properties, parameters and/or pieces of condition information (i.e., roadmap graph 700 indicates that a wide variety, if not all, intersections in a roadmap, such as prototype graph 600, can be replaced by transition curves. A robotic device using paths specified using roadmap graph 700 can take one or more transition curves of roadmap graph 700 to reduce the time to stop at and/or slowing down for intersections, such intersections 632-678 of prototype graph 600, ¶ 0108);
provisioning an operative anomaly signal sequence relative to an associated normal operation signal sequence from signal sequences of a plurality of components of the intralogistics system (i.e., Obstacle detection subsystem 134 can determine whether one or more obstacles are blocking a path and/or a trajectory of robotic device 120. Examples of these obstacles can include, but are not limited to, pallets, objects that may have fallen off a pallet, robotic devices, and human operators working in the environment. If an obstacle is detected, obstacle detection subsystem 134 can provide one or more communications indicating obstacle detection to path-following subsystem 138, ¶ 0068); and
computing, on the basis of the computed graph model, a piece of operative anomaly cause information relating to the operative anomaly signal sequence of the intralogistics system (i.e., a roadmap graph can be determined using a roadmap graph generator. In particular of these embodiments, part or all of the above-mentioned validation process can be encoded into the roadmap graph generator: For example, when the roadmap graph generator is generating track transition curves, the roadmap graph generator can select a curvature parameter related to the roadmap graph generator an intersection that is traversable quickly without causing collisions, ¶¶ 0127-128).
As per claim 2, Lalonde et al disclose provisioning of a piece of reference anomaly cause information in relation to the reference anomaly signal sequences is done additionally and a computing of the graph model is done additionally on the basis of the piece of reference anomaly cause information (i.e., when the roadmap graph generator is generating track transition curves, the roadmap graph generator can select a curvature parameter related to the roadmap graph generator an intersection that is traversable quickly without causing collisions, ¶ 0128).
As per claim 3, Lalonde et al disclose the provisioning of the piece of reference anomaly cause information is done via a human-machine interface (i.e., The user interface provided by warehouse management system 210 can provide one or more user interface functions for system 300, including, but not limited to: monitoring of robotic device(s) 120, e.g, presenting data related to location, battery status, state of charge, etc. of one or more robotic devices, ¶ 0082).
As per claim 4, Lalonde et al disclose the computing of the graph model is done with the help of methods of graph analysis, machine learning, data mining, queuing theory and/or knowledge discovery in databases (KDD) (i.e., techniques for reducing a multi-agent path finding (MAPF) problem that can, for example be used to plan routes for mobile robotic devices acting as agents, into a Boolean equation, ¶ 0047, wherein optimality of MAPF solutions using the discrete planning graph can depend on a limit of discretization, indicating that more edges means closer to optimal MAPF solutions, but also involves more computation to find the MAPF solution, ¶ 0054).
As per claim 5, Lalonde et al disclose the graph analysis in the computation of the graph model comprises a connectivity analysis, affiliation analysis and/or path analysis (i.e., techniques for reducing a multi-agent path finding (MAPF) problem that can, for example be used to plan routes for mobile robotic devices acting as agents, into a Boolean equation, ¶ 0047, wherein optimality of MAPF solutions using the discrete planning graph can depend on a limit of discretization, indicating that more edges means closer to optimal MAPF solutions, but also involves more computation to find the MAPF solution, ¶ 0054).
As per claim 9, Lalonde et al disclose the step of computing a probability of the occurrence of a respective anomaly cause (i.e., The network of poses can correspond to some or all of the locations and orientations indicated by the intersections of the prototype graph. In another example, the network of poses can be identified by sampling the environment for poses using a probabilistic roadmap technique, Rapidly-expanding Random Trees (RRT), and/or other sampling techniques, ¶ 0040).
As per claim 10, Lalonde et al disclose the graph model can be stored as an adjacency matrix, adjacency list or incidence matrix of a graph data structure (i.e., coordinated multi-agent planning for robotic devices is typically only enabled for robotic devices whose footprint cross sections are independent of their direction of motion and/or small robotic devices in an open space and/or a “gridworld” or an environment representable as a collections of uniform cells arranged in a matrix or grid, ¶ 0048).
As per claim 11, Lalonde et al disclose a provisioning of a configuration of the intralogistics system is done additionally and the computing of the graph model is done additionally on the basis of the configuration of the intralogistics system (i.e., A roadmap can provide a relatively small but expressive configuration space that is kinematically feasible for any robotic device representable using a roadmap, which can be a relatively-large class of robotic devices, ¶ 0048).
As per claim 12, Lalonde et al disclose a step of generating an optimized configuration of the intralogistics system and/or its components on the basis of the computed graph model of the intralogistics system (i.e., The poses can be connected by trajectories using a trajectory optimization framework, where constraints between adjacent poses can be used to reflect kinematics of an agent, dynamics of the agent, and/or obstacle avoidance, ¶ 0041).
As per claim 13, Lalonde et al disclose the generation of the optimized configuration of the intralogistics system is done such that a probability of an occurrence of operative anomaly signal sequences is minimized (i.e., the trajectory optimization framework can be solved using software such as the Sparse Nonlinear Optimizer software package (SNOPT), the Interior Point Optimizer software package (IPOPT), and/or other software. In other examples, the constraints can be modeled as costs of a cost function for a trajectory between poses and a lowest cost trajectory (“LCT”) can be selected as a locally optimal trajectory according to the cost function, ¶ 0041).
As per claim 14, Lalonde et al disclose the provisioning of the reference anomaly signal sequences is done such that the multiple reference anomaly signal sequences of one or multiple components at least partially relate to the same period of time (i.e., At each of one or more time steps or periods, the executor can monitor progress of each agent along its path and send path commands up to the next unsatisfied dependency. If an agent reaches the end of its path before a blocking agent completes their actions, ¶ 0186).
Claims 15-17 are rejected based upon the same rationale as the rejection of claim 1, since they are the computer program, computer-readable medium, and system claim, respectively, corresponding to the method claim.
Claims 18-20 are rejected based upon the same rationale as the rejection of claim 1 and 12, since they are the substantially similar method claims.
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 (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.
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 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Lalonde et al (US 20180172450 A1), in view of Tran et al (US 20220343275 A1).
As per claim 6, Lalonde et al does not disclose recognizing a reference anomaly signal sequence or the operative anomaly signal sequence from a signal sequence using AI-based and/or machine-learning-based pattern recognition.
Tran et al disclose FIG. 6A shows different types of learning machines that can be used, including supervised and unsupervised learning machines and reinforcement learning machines, along with sub categories. FIG. 6B shows exemplary neural network architectures. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An unsupervised machine-learning algorithm draws inferences from datasets consisting of input data without labeled responses. A reinforcement-learning algorithm allows for the automatic determination an ideal behavior within a specific context, in order to maximize performance (¶ 0153).
Lalonde et al and Tran et al are concerned with effective logistics and warehouse management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include recognizing a reference anomaly signal sequence or the operative anomaly signal sequence from a signal sequence using AI-based and/or machine-learning-based pattern recognition in Lalonde et al, as seen in Tran et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 7, Lalonde et al does not disclose the provisioning of the piece of reference anomaly cause information is done by an artificial neural network and/or a machine-learning unit and/or Al unit, which has been trained with validated combinations of anomaly signal sequences and associated pieces of anomaly cause information.
Tran et al disclose FIG. 6A shows different types of learning machines that can be used, including supervised and unsupervised learning machines and reinforcement learning machines, along with sub categories. FIG. 6B shows exemplary neural network architectures. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An unsupervised machine-learning algorithm draws inferences from datasets consisting of input data without labeled responses. A reinforcement-learning algorithm allows for the automatic determination an ideal behavior within a specific context, in order to maximize performance (¶ 0153).
Lalonde et al and Tran et al are concerned with effective logistics and warehouse management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the provisioning of the piece of reference anomaly cause information is done by an artificial neural network and/or a machine-learning unit and/or Al unit, which has been trained with validated combinations of anomaly signal sequences and associated pieces of anomaly cause information in Lalonde et al, as seen in Tran et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Lalonde et al (US 20180172450 A1), in view of Jones et al (US 20160048938 A1).
As per claim 8, Lalonde et al does not disclose the provisioning of the piece of reference anomaly cause information is done by querying a reference anomaly database with validated combinations of anomaly signal sequences and associated pieces of anomaly cause information.
Jones et al disclose a database management function can store, update and otherwise manage the data in the database 208 in accordance with a selected data model. The data structures are typically associated with one or more enterprises (e.g., material supplier, part/component manufacturer, product assembler, freight or shipping company, distributor, brand owner, wholesaler, and/or retailer) in the supply and/or logistics chain. Transactional documents, such as purchase orders, material safety data sheets, and bills of material, and agreements, such as supply and/or manufacturing agreements, or RMAs, and SLA's, contain references to all owners down the organization level, have corresponding role types and functions specified (e.g., only a buyerRole can change requestQuantity field), and include preferences and settings referenced to an appropriate level (e.g., enterprise (or the part of the enterprise involved in the supply and/or logistics chain transaction), user, etc.) (¶ 0130).
Lalonde et al and Jones et al are concerned with effective logistics and warehouse management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the provisioning of the piece of reference anomaly cause information is done by querying a reference anomaly database with validated combinations of anomaly signal sequences and associated pieces of anomaly cause information in Lalonde et al, as seen in Jones et al, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon, listed in the PTO-892, considered pertinent to applicant's disclosure, discloses logistics and warehouse management.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE D BOYCE whose telephone number is (571)272-6726. The examiner can normally be reached M-F 10a-6:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao (Rob) Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDRE D BOYCE/Primary Examiner, Art Unit 3623 March 18, 2026