QUANTUM SENSOR NETWORK AND MEASURING MULTIPLE FUNCTIONS WITH A QUANTUM SENSOR NETWORK

Application No. 18/136,257

• Filed: Apr 18, 2023
• Priority: Apr 18, 2022 — provisional 63/363,171
• Examiner: MAHARAJ, DEVIKA S
• Art Unit: 2123
• Tech Center: 2100 — Computer Architecture & Software
• Assignee: Government of the United States of America, As Represented By the Secretary of Commerce
• OA Round: 3 (Final)

Office Action

DETAILED ACTION
1. This communication is in response to the amendments filed on March 4, 2026 for Application No. 18/136,257 in which Claims 1-21 are presented for examination.

Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Response to Arguments
3. The amendments filed on March 4, 2026 have been considered. No claims have been amended. Thus, Claims 1-21 are pending and presented for examination.

4. Applicant’s arguments filed March 4, 2026 with respect to the objection to the specification have been fully considered and are persuasive. Thus, the objection to the specification has been withdrawn.

5. Applicant's arguments filed March 4, 2026 with respect to the double patenting rejection have been fully considered but they are not persuasive. As stated in the arguments, Applicant reserves the right to file a terminal disclaimer in compliance with 37 CFR 1.321(c) should the co-pending application be determined to be in condition for allowance. As such, the Examiner maintains the provisional double patenting rejection, since no such terminal disclaimer has been filed yet.

6. Applicant's arguments filed March 4, 2026 with respect to the 35 U.S.C. 103 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 2-3 of Arguments/Remarks state:
“The applicant respectfully traverses the rejection of independent claim 1 under 35 U.S.C. 103 as being allegedly obvious over Ho in view of Rubio. Independent claim 1 is directed to a process for measuring multiple functions with a quantum sensor network, requiring, inter alia, providing a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters, and calculating the multiple analytic functions from the measurements. The examiner asserts that Ho teaches a generalized quantum sensor system using quantum neural networks (QNNs) to enhance performance in noisy environments. While Ho describes pre-processing and post-processing analog signals to isolate a signal of interest from noise, Ho fails to teach or suggest configuring a plurality of quantum sensors such that each individual sensor is configured to measure a different analytic function of a set of unknown parameters. Ho’s architecture is focused on a singular sensing task, namely, enhancing the signal-to-noise ratio for detecting changes in the state of a system under test. The plurality of qubits in Ho are collectively exposed to an analog signal to extract a hidden parameter, such as a classification label or average signal amplitude, rather than being individually configured for distinct analytic functions.
Furthermore, the examiner relies on Rubio to purportedly teach providing sensors configured for different analytic functions and calculating multiple functions from measurements. However, Rubio is directed to the theoretical estimation of global properties in a networked quantum sensing model, where local parameters are encoded and inter-sensor correlations are studied. Rubio describes the simultaneous estimation of multiple linear functions of a collection of local parameters. In Rubio’s scheme, the parameters are locally encoded, one per sensor, as a collection of local properties. Crucially, Rubio’s sensors are not individually configured to measure a different analytic function as required by claim 1; rather, the functions are mathematical combinations of the local parameters that are estimated globally after the sensing event. The configuration of sensors in claim 1 requires a proactive structural or operational setup of the sensors themselves to target specific analytic functions prior to calculation.
Even if the teachings of Ho and Rubio were combined, such a combination would not result in the invention defined in claim 1. Ho provides a methodology for noise filtration in a single sensing channel using QNNs, while Rubio provides a theoretical framework for global estimation over networked sensors. Neither reference, alone or in combination, suggests a quantum sensor network where the sensors are individually and specifically configured for different analytic functions of a set of unknown parameters. The examiner’s reliance on Rubio to fill this gap is misplaced because Rubio’s “different analytic function” is a result of post-processing calculations of local properties, not a configuration requirement of the sensors themselves. Because independent claim 1 contains limitations neither found nor suggested by the cited prior art, independent claim 1 is nonobvious.”
Examiner respectfully disagrees. Instant claim 1 states “a process for measuring multiple functions with a quantum sensor network, the process comprising: providing a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters” – considering broadest reasonable interpretation of the limitation, Rubio clearly teaches each of the quantum sensors of the network being configured for measuring a different analytic function of a set of unknown parameters. Rubio Pg. 1 states “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection of local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric.” – further, Figure 1 on Pg. 2 depicts a network of d = 5 sensors, the parameters at each sensor represent local properties since each of them is locally encoded in a single sensor. As such, since each quantum sensor comprises different local properties encoded in a single sensor, when aggregated to consider global properties and/or when used simultaneously to estimate multiple linear functions, this still equates to each quantum sensor being configured for measuring a different analytic function of a set of unknown parameters, as each sensor comprises different local encodings, meaning that each sensor is weighted differently and/or interacts with local parameters uniquely – hence, considering broadest reasonable interpretation, each sensor is responsible for measuring a function with varied or “different” parameters. Furthermore, the combination of Ho and Rubio is valid, as both references teach enhancing performance of quantum sensor operations and are both directed to analogous operations for measuring functions with a quantum sensor network – although Ho does not explicitly disclose measuring a different analytic function of a set of unknown parameters, Ho still discloses processing analog input signals using the plurality of qubits/quantum sensors to generate a phase shift of each qubit by unknown parameters. This still results in a valid combination of references to teach the instant claims, as Rubio lies in the same field of endeavor, especially considering the broad/generic claim language, as currently drafted.

Applicant’s Arguments on Pgs. 3-4 of Arguments/Remarks state:
“The Applicant respectfully traverses the rejection of independent claim 7 under 35 U.S.C. 103 as being allegedly obvious over Ho in view of Rubio. Independent claim 7 specifies a quantum sensor network comprising a plurality of quantum sensors, each configured to measure a different function of a set of unknown parameters, a network topology, and a controller configured to prepare the sensors, expose them to the parameters, and use the measurements to calculate the multiple functions. The examiner asserts that Ho teaches a quantum sensor system including a quantum processing unit (QPU) that receives variational parameters used in pre- and post-processing phases with sets of qubits. While Ho describes qubits that can be exposed to analog signals, Ho fails to disclose a network architecture where each quantum sensor is individually configured to measure a different function. As discussed in the traversal of claim 1, Ho focuses on using quantum neural networks to filter noise from a signal of interest to sense a change in a state of a system under test. This constitutes a collective sensing operation rather than a network of sensors configured for distinct analytic functions.
Furthermore, the examiner uses Rubio to teach a plurality of quantum sensors where each is configured for a different function and the use of measurements to calculate multiple functions. Rubio discusses a model for networked quantum sensing where a single parameter is locally encoded in each sensor, forming a collection of local properties, while global properties are thought of as non-trivial functions of these parameters. Crucially, Rubio’s “functions” are mathematical abstractions estimated globally after the local parameters are already encoded in the sensors. Rubio lacks any teaching or suggestion of a controller that proactively configures individual quantum sensors in the network to measure a specific function prior to exposure. Rubio’s sensors merely encode individual local parameters, and any “different functions” emerge only during post-measurement classical estimation.
The combination of Ho and Rubio does not render the limitations of claim 7 obvious. Ho’s controller (the QPU) is configured to optimize a signal-to-noise ratio via quantum neural networks for a singular sensing goal. It is not configured to manage a network of sensors each targeting different analytic functions of a set of unknown parameters as required by claim 7. Rubio’s theoretical framework for multi-parameter metrology does not supply this structural configuration requirement. Because the prior art does not suggest a quantum sensor network with a controller configured to use measurements to calculate multiple functions from sensors that were themselves each configured for a different function, claim 7 is nonobvious.”
Examiner respectfully disagrees for substantially the same reasons as stated above. Furthermore, Rubio is not relied upon for teaching the generically recited “controller” presented by Independent Claim 7 – instead, Ho teaches the use of a quantum processing unit (QPU) for performing the processing presented within their disclosure. Examiner cited Ho Col. 3 lines 38-42 which detail how the approach presented by Ho may be implemented using a quantum processing unit of a quantum computer – this is merely one such recitation, but the Ho reference points to the use of the QPU several times for their processing. Moreover, it must be highlighted that the controller of instant Independent Claim 7 is very broadly/generically recited – the controller is seemingly already configured to perform the operations of the claim and no further details are provided in the claim language regarding this controller. Applicant’s specification also does not further detail and/or limit this controller – the term is merely recited without significantly more, and Par. [0031] merely mentions one embodiment in which the controller may be a computer. Contrary to Applicant’s arguments, the quantum processing unit (QPU) of Ho is not only limited/configured to optimize a signal-to-noise ratio for a singular sensing goal – a quantum processing unit is a known term of art, comprising a specialized computing chip/device to execute quantum algorithms using quantum principles (such as superposition, entanglement, etc.) Thus, as Ho’s QPU is used to control/perform processing of such algorithms, the QPU is analogous to the controller presented by the instant claim limitations.

Applicant’s Arguments on Pgs. 4-5 of Arguments/Remarks state:
“The Applicant respectfully traverses the rejection of independent claim 15 under 35 U.S.C. 103 as being allegedly obvious over Ho in view of Rubio. Independent claim 15 is directed to a process for making a quantum sensor network that measures multiple functions, requiring, among other steps, providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters. The examiner contends that Ho teaches a process for making a quantum sensor network and providing a plurality of sensors.
While Ho describes a quantum sensor system comprising a quantum processing unit coupled to a plurality of qubits, this disclosure focuses on using quantum neural networks to pre- and post-process analog signals for a collective sensing task. Ho does not disclose or suggest a manufacturing process that endows individual sensors with the capability to measure different functions of unknown parameters. The sensors in Ho are treated as a collective set used to sense changes in the state of a system under test. Moreover, the examiner’s reliance on Rubio to teach sensors capable of measuring different functions is misplaced. Rubio discusses a model for networked sensing where parameters are locally encoded in individual sensors, forming local properties. Although Rubio considers the simultaneous estimation of multiple linear functions, these functions are global properties derived from the collective parameters of the network. Rubio does not teach providing sensors that are each capable of measuring a different function as a specific configuration or capability of the sensor itself. In Rubio, the functions are mathematical results of post-measurement calculations, not a characteristic of the individual sensors provided during a network assembly process. Consequently, even when combined, Ho and Rubio fail to teach or suggest a process for making a quantum sensor network where the manufacturing step of providing sensors includes endowing each sensor with the capability for a different functional measurement. Because independent claim 15 contains limitations not disclosed or suggested by the cited art, claim 15 is nonobvious.”
Examiner respectfully disagrees for substantially the same reasons as presented above. Furthermore, Independent Claim 15 does not in any way suggest or recite a “manufacturing process”, instead the claim simply states “a process for making a quantum sensor network that measures multiple functions […]” – this does not suggest such a “manufacturing process” as argued by Applicant. Using broadest reasonable interpretation of the claim language, the term “making” may just indicate “forming” or “connecting” multiple quantum sensors together to form such a quantum sensor network, similar to the limitations presented by Independent Claim 7 – again, this does not indicate any sort of “manufacturing process”. Applicant’s arguments are overstating the very broad instant claim language – if Applicant wishes for the claim to recite a “manufacturing process”, this must be clear and well-defined by the claim language, as well as being supported by Applicant’s specification. The currently drafted claim language does not recite, nor indicate, such limitations.

Applicant’s Arguments on Pgs. 5-6 of Arguments/Remarks state:
“Claims 2 through 6, 8 through 14, and 16 through 21 are each properly allowable, as they depend from independent claims 1, 7, or 15, which have been demonstrated to be patentably distinct over the cited prior art. Specifically, regarding claims 5 and 13, which define analytic functions as nonlinear combinations of a set of unknown parameters, the allowability is maintained over the combination of Ho, Rubio, and Ashrafi. While the examiner relies on Ashrafi to teach systems for predicting behavior using artificial intelligence implementing nonlinear modeling, this reference relates to the retrospective analysis of data streams rather than the proactive configuration of sensors required by the present invention. Similarly, the rejections of claims 8 through 10 and 16 through 18, which recite various array geometries, rely on Zhang to teach linear or multi-dimensional sensor arrangements. However, because Zhang fails to cure the fundamental deficiencies of Ho and Rubio regarding the proactive sensor configuration and functional measurement diversity of the parent claims, these dependent limitations remain novel and nonobvious. Finally, the rejection of claim 20 based on Gao's teaching of network topologies is rendered moot by the allowability of independent claim 15. The applicant maintains that the prior art fails to teach or suggest the innovation defined in the base claims, and thus, all dependent claims are in condition for allowance.”
Examiner respectfully disagrees for substantially the same reasons as stated above. Further, regarding Applicant’s arguments with respect to the dependent claims, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Thus, the 35 U.S.C. 103 rejection is maintained.

Double Patenting
7. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
8. Claims 1-3, 6-11, and 14-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1 and 14-28 of copending Application No. 18/232,890 in view of Rubio et al. (“Quantum sensing networks for the estimation of linear functions”). Although the claims at issue are not identical, they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in copending Application No. 18/232,890 since the instant application claims common subject matter.
This is a provisional nonstatutory double patenting rejection.
The bolded portions below highlight the differences between the instant application and the copending application, which illustrates the obvious and anticipatory relationship of the claim limitations at issue:
Instant Application (18/136,257)
Copending Application (18/232,890)
Claim 1: A process for measuring multiple functions with a quantum sensor network, the process comprising:

providing a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters;

preparing the plurality of quantum sensors in a known state;

exposing the plurality of quantum sensors to the set of unknown parameters;

measuring the plurality of quantum sensors; and

calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors.
A process for measuring a single linear function
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of unknown parameters
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with a quantum sensor network while using the minimum amount of entanglement, the process comprising:

providing a plurality of d quantum sensors, wherein each quantum sensor j is configured for measuring
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;

preparing the plurality of quantum sensors in a probe quantum state
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with a minimum amount of entanglement, such that the amount of entanglement is the smallest amount of entanglement that gives the same optimal measurement of the linear function
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as if the amount of entanglement was not restricted;

exposing the plurality of quantum sensors to the set of unknown parameters;

measuring the plurality of quantum sensors; and

calculating the single linear function
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from the measurements of the plurality of quantum sensors with robust phase estimation.
Copending Application 18/232,890 discloses the same limitations as the instant application except that the instant application specifies that the quantum sensor network includes a plurality of quantum sensors for measuring multiple different analytic functions, rather than just a single linear function. However, Rubio et al. (“Quantum sensing networks for the estimation of linear functions”) teaches providing a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different analytic function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed) and calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors (Rubio, Pg. 22, “The central question addressed in this work has been that of the role of inter-sensor correlations in the estimation of linear functions with arbitrary geometry, having exploited a sensor symmetric qubit network in the presence of different amounts of data. First we focused on the asymptotic part of the problem, and by optimising the class of sensor-symmetric states, we have established an optimal link between correlation strength and the geometry of the linear functions.”, thus, multiple analytic functions of the set of unknown parameters (disclosed on Rubio Pg. 2) are calculated/estimated based on the measurements of the plurality of quantum sensors/qubits).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process including providing a plurality of quantum sensors, preparing the plurality of quantum sensors in a known (probe quantum) state, exposing the plurality of quantum sensors to a set of unknown parameters, measuring the plurality of quantum sensors, and calculating the linear function from the measurements, as disclosed by copending application 18/232,890, to include providing a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters and calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”)
Claim 2: The process of claim 1, wherein the plurality of quantum sensors is arranged in a network.
Claim 14: The process of claim 1, wherein the plurality of quantum sensors is arranged in a network.
Claim 3: The process of claim 1, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 15: The process of claim 1, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 6: The process of claim 1, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields.
Claim 16: The process of claim 1, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields.
Claim 7: A quantum sensor network comprising: a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters;

a network topology that connects the plurality of quantum sensors; and

a controller that is configured to:
prepare the plurality of quantum sensors in a known state;

expose the plurality of quantum sensors to the set of unknown parameters;

measure the plurality of quantum sensors; and

use the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters.
Claim 17: A quantum sensor network comprising: a plurality of quantum sensors, each quantum sensor j is configured for measuring
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out of a set of unknown parameters
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, such that the plurality of quantum sensors is configured to be in a probe quantum state
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with a minimum amount of entanglement, such that the amount of entanglement is the smallest amount of entanglement that gives the same optimal measurement of the linear function
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as if the amount of entanglement was not restricted;

a network topology that connects the plurality of quantum sensors; and
a controller that is configured to:

prepare the plurality of quantum sensors in the probe quantum state
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;

expose the plurality of quantum sensors to the set of unknown parameters
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;

measure the plurality of quantum sensors; and
use the measurements of the plurality of quantum sensors to calculate the function
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of the set of unknown parameters.
Copending Application 18/232,890 discloses the same limitations as the instant application except that the instant application specifies that the quantum sensor network includes a plurality of quantum sensors for measuring multiple different analytic functions, rather than just a single linear function. However, Rubio et al. (“Quantum sensing networks for the estimation of linear functions”) teaches a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed) and using the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters (Rubio, Pg. 22, “The central question addressed in this work has been that of the role of inter-sensor correlations in the estimation of linear functions with arbitrary geometry, having exploited a sensor symmetric qubit network in the presence of different amounts of data. First we focused on the asymptotic part of the problem, and by optimising the class of sensor-symmetric states, we have established an optimal link between correlation strength and the geometry of the linear functions.”, thus, multiple functions of the set of unknown parameters (disclosed on Rubio Pg. 2) are calculated/estimated based on the measurements of the plurality of quantum sensors/qubits).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network including providing a plurality of quantum sensors, preparing the plurality of quantum sensors in a known (probe quantum) state, exposing the plurality of quantum sensors to a set of unknown parameters, measuring the plurality of quantum sensors, and calculating the linear function from the measurements, as disclosed by copending application 18/232,890, to include a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters and using the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”)
Claim 8: The quantum sensor network of claim 7, wherein the plurality of quantum sensors is arranged in a linear array.
Claim 18: The quantum sensor network of claim 17, wherein the plurality of quantum sensors is arranged in a linear array.
Claim 9: The quantum sensor network of claim 7, wherein the plurality of quantum sensors is arranged in a two-dimensional array.
Claim 19: The quantum sensor network of claim 17, wherein the plurality of quantum sensors is arranged in a two-dimensional array.
Claim 10: The quantum sensor network of claim 7, wherein the plurality of quantum sensors is arranged in a three-dimensional array.
Claim 20: The quantum sensor network of claim 17, wherein the plurality of quantum sensors is arranged in a three-dimensional array.
Claim 11: The quantum sensor network of claim 7, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 21: The quantum sensor network of claim 17, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 14: The quantum sensor network of claim 7, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields.
Claim 22: The quantum sensor network of claim 17, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields.
Claim 15: A process for making a quantum sensor network that measures multiple functions, the process comprising:

providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters;

arranging the plurality of quantum sensors in a network topology; and

connecting the plurality of quantum sensors to a controller.
Claim 23: A process for making a quantum sensor network that measures a single linear function
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, the process comprising:

providing a plurality of d quantum sensors;

arranging the plurality of quantum sensors j is configured for measuring
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out of a set of unknown parameters
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;

connecting the plurality of quantum sensors to a controller;

preparing, by the controller, the plurality of quantum sensors in a probe quantum state
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with a minimum amount of entanglement, such that the amount of entanglement is the smallest amount of entanglement that gives the same optimal measurement of the linear function
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as if the amount of entanglement was not restricted.
Copending Application 18/232,890 discloses the same limitations as the instant application except that the instant application specifies that the quantum sensor network includes a plurality of quantum sensors for measuring multiple different analytic functions, rather than just a single linear function. However, Rubio et al. (“Quantum sensing networks for the estimation of linear functions”) teaches providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process for making a quantum sensor network, as disclosed by copending application 18/232,890, to include providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”)
Claim 16: The process of claim 15, wherein the plurality of quantum sensors is arranged in a linear array.
Claim 24: The process of claim 23, wherein the plurality of quantum sensors is arranged in a linear array.
Claim 17: The process of claim 15, wherein the plurality of quantum sensors is arranged in a two-dimensional array.
Claim 25: The process of claim 23, wherein the plurality of quantum sensors is arranged in a two-dimensional array.
Claim 18: The process of claim 15, wherein the plurality of quantum sensors is arranged in a three-dimensional array.
Claim 26: The process of claim 23, wherein the plurality of quantum sensors is arranged in a three-dimensional array.
Claim 19: The process of claim 15, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 27: The process of claim 23, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors.
Claim 20: The process of claim 15, wherein the network topology is a star topology, a ring topology, or a mesh topology.
Claim 28: The process of claim 23, wherein the network topology is a star topology, a ring topology, or a mesh topology.
Claim 21: The process of claim 15, wherein the controller is a computer.
Claim 29: The process of claim 23, wherein the controller is a classical computer.

Claim Rejections - 35 USC § 103
9. 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.

10. Claims 1-4, 6-7, 11-12, 14-15, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (hereinafter Ho) (US Patent 12456068), in view of Rubio et al. (hereinafter Rubio) (“Quantum sensing networks for the estimation of linear functions”).
Regarding Claim 1, Ho teaches a process for measuring multiple functions with a quantum sensor network (Ho, Col. 1 lines 13-15, “Quantum sensors are devices configured to respond to one or more input signals in order to perform a measurement of a physical quantity associated with such signals.” & Col. 1 lines 37-43, “The technology relates to quantum sensors and quantum neural networks, including enhancing performance of quantum sensors in noisy environments. In particular, a quantum neural network (QNN), which is a set of instructions for a sequence of parametrized quantum gates, is used to pre- and post-process analog signals to which the quantum registers (qubits) of a quantum sensor are exposed”, thus, a process/method for measuring multiple functions with a quantum sensor network is disclosed), the process comprising:
providing a plurality of quantum sensors (Ho, Col. 3 lines 54-59, “FIG. 1 illustrates a generalized example 100 of a quantum sensor system, which includes a quantum processing unit (QPU) 102 that receives one or more variational parameters 104. The variational parameters are used in pre- and post-processing phases with one or more sets of qubits of the quantum processing unit 102.”, thus, a plurality of quantum sensors are disclosed, as a plurality of qubits comprising a quantum sensor system is depicted by Ho Figure 1. Further, Col. 3 lines 4-6 states “In a further example, the plurality of qubits includes a group of computational qubits and a group of sensing qubits different than the group of computational qubits.” – hence, the plurality of sensing qubits are analogous to the plurality of quantum sensors, as also supported by Applicant’s specification Par. [0027-0028]), each of which is configured for measuring a different analytic function of a set of unknown parameters (Ho discloses processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54 – however, this does not explicitly recite where each of the quantum sensors is configured for measuring a different analytic function of a set of unknown parameters. See introduction of Rubio reference below for explicit recitation of where each quantum sensor is configured for measuring a different analytic function of a set of unknown parameters);
preparing the plurality of quantum sensors in a known state (Ho, Col. 11 lines 23-28, “At block 602, the system performs pre-processing on a set of qubits of the quantum processing unit based on one or more variational parameters of a first quantum neural network. The first quantum neural network is selected to prepare entangled states that are sensitive to a signal of interest.”, therefore, the plurality of quantum sensors/qubits are prepared in a known state (entangled state) – See Figure 6 label 602);
exposing the plurality of quantum sensors to the set of unknown parameters (Ho, Col. 11 lines 28-30, “At block 604, after pre-processing, the set of qubits is exposed to an analog signal that includes the signal of interest and noise.”, therefore, the plurality of quantum sensors/qubits are exposed to the analog signal that includes the signal of interest and noise – See Figure 6 label 604. This may extract hidden/unknown parameters in the signal, such as field amplitudes – as supported by Ho Col. 8 lines 58-64 and Ho Col. 10 lines 8-15);
measuring the plurality of quantum sensors (Ho, Col. 11 lines 30-35, “At block 606, post-processing is performed on the set of qubits based on one or more variational parameters of a second quantum neural network. The post-processing filters out at least some of the noise of the analog signal. At block 608, a filtered signal is output as a result of the post-processing.”, thus, the plurality of quantum sensors are measured by the post-processing step – See Figure 6 labels 606 and 608. Further, as mentioned by Ho Col. 5 lines 18-22, the post-processed filtered signal produced by the quantum sensors is measured, to be used for subsequent tasks); and
calculating the multiple analytic functions (See introduction of Rubio reference below for teaching of calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors) of the set of unknown parameters from the measurements of the plurality of quantum sensors (Ho, Col. 11 lines 35-39, “At block 610, the process further includes detecting induced dynamics associated with exposing the set of qubits to the analog signal based on the filtered signal. And at block 612, the detected induced dynamics are used to sense a change in a state of a system under test.”, therefore, the induced dynamics (phase shifts, entanglement, etc.) may be detected/calculated based on the set of unknown parameters from the measurements of the plurality of quantum sensors, associated with the filtered signal).

Ho teaches processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54. However, Ho does not explicitly disclose:
a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters.
calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors
However, Rubio teaches:
a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different analytic function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed).
calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors (Rubio, Pg. 22, “The central question addressed in this work has been that of the role of inter-sensor correlations in the estimation of linear functions with arbitrary geometry, having exploited a sensor symmetric qubit network in the presence of different amounts of data. First we focused on the asymptotic part of the problem, and by optimising the class of sensor-symmetric states, we have established an optimal link between correlation strength and the geometry of the linear functions.”, thus, multiple analytic functions of the set of unknown parameters (disclosed on Rubio Pg. 2) are calculated/estimated based on the measurements of the plurality of quantum sensors/qubits)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process for measuring multiple functions per claim 1, as disclosed by Ho to include a plurality of quantum sensors, each of which is configured for measuring a different analytic function of a set of unknown parameters and calculating the multiple analytic functions of the set of unknown parameters from the measurements of the plurality of quantum sensors, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”).

Regarding Claim 2, Ho in view of Rubio teaches the process of claim 1, wherein the plurality of quantum sensors is arranged in a network (Ho, Col. 4 lines 12-22, “The central question addressed in this work has been that of the role of inter-sensor correlations in the estimation of linear functions with arbitrary geometry, having exploited a sensor symmetric qubit network in the presence of different amounts of data. First we focused on the asymptotic part of the problem, and by optimising the class of sensor-symmetric states, we have established an optimal link between correlation strength and the geometry of the linear functions.”, therefore, the plurality of quantum sensors/qubits is arranged in a quantum neural network).

Regarding Claim 3, Ho in view of Rubio teaches the process of claim 1, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors (Ho, Col. 1 lines 49-53, “According to aspects of the technology, quantum sensors comprising a set of qubits utilize certain forms of highly correlated quantum states to become very sensitive to fluctuations of certain classical fields (e.g., magnetic, electric, electromagnetic, gravitational or optical).”, therefore, the plurality of quantum sensors may comprise a set of qubits).

Regarding Claim 4, Ho in view of Rubio teaches the process of claim 1, wherein the multiple analytic functions are linear combinations of the set of unknown parameters (Rubio, Pg. 2, “These protocols are normally formulated on the basis of d unknown parameters θ = (θ1,...,θd) that arise naturally in the description of the system at hand, and in many cases these are the quantities of interest […] Beyond these two types of global properties, the simultaneous estimation of l > 1 linear but otherwise arbitrary real functions has been a less travelled path. There exist generic bounds for this problem (see, e.g., [32, 60]), which in practice may arise in scenarios such as the estimation of phase differences [29, 60]. However, how quantum correlations may help for linear functions with arbitrary geometry has not been examined in detail.”, thus, the multiple analytic functions may comprise linear combinations of the set of unknown parameters).
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.

Regarding Claim 6, Ho in view of Rubio teaches the process of claim 1, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields (Ho, Col. 1 lines 49-53, “According to aspects of the technology, quantum sensors comprising a set of qubits utilize certain forms of highly correlated quantum states to become very sensitive to fluctuations of certain classical fields (e.g., magnetic, electric, electromagnetic, gravitational or optical).” & Col. 10 lines 8-11, “A sample use case of the system involves the detection of fluctuations of a DC signal (constant in time for some time). By way of example, this could be for the measurement of electric and/or magnetic field amplitude.”, therefore, the set of unknown parameters may comprise a set of classical fields (e.g., magnetic, electric, electromagnetic, gravitational, or optical) and more specifically, measurement of electric and/or magnetic field amplitude. Examiner notes that Rubio also teaches the use of unknown parameters on Rubio Pg. 2)
Regarding Claim 7, Ho teaches a quantum sensor network (Ho, Col. 3 lines 54-57, “FIG. 1 illustrates a generalized example 100 of a quantum sensor system, which includes a quantum processing unit (QPU) 102 that receives one or more variational parameters 104.”, thus, a quantum sensor network is disclosed) comprising:
a plurality of quantum sensors (Ho, Col. 3 lines 54-59, “FIG. 1 illustrates a generalized example 100 of a quantum sensor system, which includes a quantum processing unit (QPU) 102 that receives one or more variational parameters 104. The variational parameters are used in pre- and post-processing phases with one or more sets of qubits of the quantum processing unit 102.”, thus, a plurality of quantum sensors are disclosed, as a plurality of qubits comprising a quantum sensor system is depicted by Ho Figure 1. Further, Col. 3 lines 4-6 states “In a further example, the plurality of qubits includes a group of computational qubits and a group of sensing qubits different than the group of computational qubits.” – hence, the plurality of sensing qubits are analogous to the plurality of quantum sensors, as also supported by Applicant’s specification Par. [0027-0028]), each of which is configured to measure a different function of a set of unknown parameters (Ho discloses processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54 – however, this does not explicitly recite where each of the quantum sensors is configured for measuring a different function of a set of unknown parameters. See introduction of Rubio reference below for explicit recitation of where each quantum sensor is configured for measuring a different function of a set of unknown parameters);
a network topology that connects the plurality of quantum sensors (Ho, Col. 1 lines 37-43, “The technology relates to quantum sensors and quantum neural networks, including enhancing performance of quantum sensors in noisy environments. In particular, a quantum neural network (QNN), which is a set of instructions for a sequence of parametrized quantum gates, is used to pre- and post-process analog signals to which the quantum registers (qubits) of a quantum sensor are exposed.”, therefore, a network topology connecting the plurality of quantum sensors is disclosed, as the quantum neural network connects a plurality of qubits/quantum sensors); and
a controller (Ho, Col. 3 lines 38-42, “This approach can be implemented using a quantum processing unit (QPU) of a quantum computer. For purposes of this disclosure, a quantum computer includes any form of quantum memory over which the designer has sufficient control and which demonstrates quantum coherence”, therefore, a controller, such as a quantum processing unit of a quantum computer, may be configured to perform the below processes. The operations of the quantum processing unit are also depicted by Ho Figures 1 and 2) that is configured to:
prepare the plurality of quantum sensors in a known state (Ho, Col. 11 lines 23-28, “At block 602, the system performs pre-processing on a set of qubits of the quantum processing unit based on one or more variational parameters of a first quantum neural network. The first quantum neural network is selected to prepare entangled states that are sensitive to a signal of interest.”, therefore, the plurality of quantum sensors/qubits are prepared in a known state (entangled state) – See Figure 6 label 602);
expose the plurality of quantum sensors to the set of unknown parameters (Ho, Col. 11 lines 28-30, “At block 604, after pre-processing, the set of qubits is exposed to an analog signal that includes the signal of interest and noise.”, therefore, the plurality of quantum sensors/qubits are exposed to the analog signal that includes the signal of interest and noise – See Figure 6 label 604. This may extract hidden/unknown parameters in the signal, such as field amplitudes – as supported by Ho Col. 8 lines 58-64 and Ho Col. 10 lines 8-15);
measure the plurality of quantum sensors (Ho, Col. 11 lines 30-35, “At block 606, post-processing is performed on the set of qubits based on one or more variational parameters of a second quantum neural network. The post-processing filters out at least some of the noise of the analog signal. At block 608, a filtered signal is output as a result of the post-processing.”, thus, the plurality of quantum sensors are measured by the post-processing step – See Figure 6 labels 606 and 608. Further, as mentioned by Ho Col. 5 lines 18-22, the post-processed filtered signal produced by the quantum sensors is measured, to be used for subsequent tasks); and
use the measurements of the plurality of quantum sensors to calculate the multiple functions (See introduction of Rubio reference below for teaching of calculating the multiple functions of the set of unknown parameters using the measurements of the plurality of quantum sensors) of the set of unknown parameters (Ho, Col. 11 lines 35-39, “At block 610, the process further includes detecting induced dynamics associated with exposing the set of qubits to the analog signal based on the filtered signal. And at block 612, the detected induced dynamics are used to sense a change in a state of a system under test.”, therefore, the induced dynamics (phase shifts, entanglement, etc.) may be detected/calculated based on the set of unknown parameters from the measurements of the plurality of quantum sensors, associated with the filtered signal).

Ho teaches processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54. However, Ho does not explicitly disclose:
a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters
use the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters.
However, Rubio teaches:
a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed)
use the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters (Rubio, Pg. 22, “The central question addressed in this work has been that of the role of inter-sensor correlations in the estimation of linear functions with arbitrary geometry, having exploited a sensor symmetric qubit network in the presence of different amounts of data. First we focused on the asymptotic part of the problem, and by optimising the class of sensor-symmetric states, we have established an optimal link between correlation strength and the geometry of the linear functions.”, thus, multiple functions of the set of unknown parameters (disclosed on Rubio Pg. 2) are calculated/estimated based on the measurements of the plurality of quantum sensors/qubits).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network for measuring multiple functions per claim 7, as disclosed by Ho to include a plurality of quantum sensors, each of which is configured to measure a different function of a set of unknown parameters and using the measurements of the plurality of quantum sensors to calculate the multiple functions of the set of unknown parameters, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”)

Regarding Claim 11, Ho in view of Rubio teaches the quantum sensor network of claim 7, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors (Ho, Col. 1 lines 49-53, “According to aspects of the technology, quantum sensors comprising a set of qubits utilize certain forms of highly correlated quantum states to become very sensitive to fluctuations of certain classical fields (e.g., magnetic, electric, electromagnetic, gravitational or optical).”, therefore, the plurality of quantum sensors may comprise a set of qubits).

Regarding Claim 12, Ho in view of Rubio teaches the quantum sensor network of claim 7, wherein the multiple functions are linear combinations of the set of unknown parameters (Rubio, Pg. 2, “These protocols are normally formulated on the basis of d unknown parameters θ = (θ1,...,θd) that arise naturally in the description of the system at hand, and in many cases these are the quantities of interest […] Beyond these two types of global properties, the simultaneous estimation of l > 1 linear but otherwise arbitrary real functions has been a less travelled path. There exist generic bounds for this problem (see, e.g., [32, 60]), which in practice may arise in scenarios such as the estimation of phase differences [29, 60]. However, how quantum correlations may help for linear functions with arbitrary geometry has not been examined in detail.”, thus, the multiple analytic functions may comprise linear combinations of the set of unknown parameters).
The reasons of obviousness have been noted in the rejection of Claim 7 above and applicable herein.

Regarding Claim 14, Ho in view of Rubio teaches the quantum sensor network of claim 7, wherein the set of unknown parameters is a set of field amplitudes, a set of temperatures, a set of pressures, a set of strains, a set of forces, a set of magnetic fields, a set of electric fields, or a set of gravitational fields (Ho, Col. 1 lines 49-53, “According to aspects of the technology, quantum sensors comprising a set of qubits utilize certain forms of highly correlated quantum states to become very sensitive to fluctuations of certain classical fields (e.g., magnetic, electric, electromagnetic, gravitational or optical).” & Col. 10 lines 8-11, “A sample use case of the system involves the detection of fluctuations of a DC signal (constant in time for some time). By way of example, this could be for the measurement of electric and/or magnetic field amplitude.”, therefore, the set of unknown parameters may comprise a set of classical fields (e.g., magnetic, electric, electromagnetic, gravitational, or optical) and more specifically, measurement of electric and/or magnetic field amplitude. Examiner notes that Rubio also teaches the use of unknown parameters on Rubio Pg. 2).

Regarding Claim 15, Ho teaches a process for making a quantum sensor network that measures multiple functions (Ho, Col. 3 lines 54-57, “FIG. 1 illustrates a generalized example 100 of a quantum sensor system, which includes a quantum processing unit (QPU) 102 that receives one or more variational parameters 104.”, thus, a quantum sensor network is disclosed), the process comprising:
providing a plurality of quantum sensors (Ho, Col. 3 lines 54-59, “FIG. 1 illustrates a generalized example 100 of a quantum sensor system, which includes a quantum processing unit (QPU) 102 that receives one or more variational parameters 104. The variational parameters are used in pre- and post-processing phases with one or more sets of qubits of the quantum processing unit 102.”, thus, a plurality of quantum sensors are disclosed, as a plurality of qubits comprising a quantum sensor system is depicted by Ho Figure 1. Further, Col. 3 lines 4-6 states “In a further example, the plurality of qubits includes a group of computational qubits and a group of sensing qubits different than the group of computational qubits.” – hence, the plurality of sensing qubits are analogous to the plurality of quantum sensors, as also supported by Applicant’s specification Par. [0027-0028]), each of which is capable of measuring a different function of a set of unknown parameters (Ho discloses processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54 – however, this does not explicitly recite where each of the quantum sensors is configured for measuring a different function of a set of unknown parameters. See introduction of Rubio reference below for explicit recitation of where each quantum sensor is configured for measuring a different function of a set of unknown parameters);
arranging the plurality of quantum sensors in a network topology (Ho, Col. 1 lines 37-43, “The technology relates to quantum sensors and quantum neural networks, including enhancing performance of quantum sensors in noisy environments. In particular, a quantum neural network (QNN), which is a set of instructions for a sequence of parametrized quantum gates, is used to pre- and post-process analog signals to which the quantum registers (qubits) of a quantum sensor are exposed.”, therefore, the plurality of quantum sensors may be arranged in a network topology, as a quantum neural network may be utilized to connect a plurality of qubits/quantum sensors); and
connecting the plurality of quantum sensors to a controller (Ho, Col. 5 lines 40-46, “FIG. 2 illustrates an example functional arrangement 200 for, e.g., quantum processing unit 102 of FIG. 1. Here, a classical processing unit 202 of one or more processing devices is operatively coupled to the quantum processing unit, which includes one or more sets of qubits (quantum registers)”, therefore, a controller, such as a quantum processing unit of a quantum computer, may be connected to a plurality of quantum sensors/qubits. This is better depicted by Ho Figures 1 and 2 which depict how the quantum processing unit (QPU) is connected to a plurality of quantum registers/sensors).

Ho teaches processing analog input signals using the plurality of qubits to generate a phase shift of each qubit by a value depending on the amplitude of the field (an unknown parameter, as supported by Applicant’s instant claim 6), as per Ho Col. 4 lines 44-54. However, Ho does not explicitly disclose:
providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters
However, Rubio teaches:
providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters (Rubio, Pg. 1, Abstract, “Among these, a problem of great significance, both fundamentally and for constructing efficient sensing networks, is that of the role of inter-sensor correlations in the simultaneous estimation of multiple linear functions, where the latter are taken over a collection local parameters and can thus be seen as global properties. In this work we provide a solution to this when each node is a qubit and the state of the network is sensor-symmetric. First we derive a general expression linking the amount of inter-sensor correlations and the geometry of the vectors associated with the functions, such that the asymptotic error is optimal.”, thus, a plurality of quantum sensors/qubits, each of which is configured for measuring a different function of a set of unknown parameters (disclosed on Rubio Pg. 2) are disclosed)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified process for making a quantum sensor network that measures multiple functions per claim 15, as disclosed by Ho to include providing a plurality of quantum sensors, each of which is capable of measuring a different function of a set of unknown parameters, as disclosed by Rubio. One of ordinary skill in the art would have been motivated to make this modification to enable the simultaneous estimation/calculation of multiple different linear functions which may efficiently combine different measurements from multiple quantum sensors/qubits to more accurately depict correlations in multiparameter schemes and enhance output precision with limited data (Rubio, Pg. 4, “Our approach to the simultaneous estimation of linear functions in a scheme for distributed quantum sensing will serve as a basis to investigate how to harness correlations in multiparameter schemes, operating both in and out of the asymptotic regime. Since the construction of entangled networks is likely to be difficult in practice, these insights may prove to be crucial in the study and implementation of quantum sensing networks that operate with a realistic amount of data.”).

Regarding Claim 19, Ho in view of Rubio teaches the process of claim 15, wherein the plurality of quantum sensors is qubits, interferometers, or field-quadrature displacement sensors (Ho, Col. 1 lines 49-53, “According to aspects of the technology, quantum sensors comprising a set of qubits utilize certain forms of highly correlated quantum states to become very sensitive to fluctuations of certain classical fields (e.g., magnetic, electric, electromagnetic, gravitational or optical).”, therefore, the plurality of quantum sensors may comprise a set of qubits).

Regarding Claim 21, Ho in view of Rubio teaches the process of claim 15, wherein the controller is a computer (Ho, Col. 3 lines 38-42, “This approach can be implemented using a quantum processing unit (QPU) of a quantum computer. For purposes of this disclosure, a quantum computer includes any form of quantum memory over which the designer has sufficient control and which demonstrates quantum coherence”, therefore, the controller, used to perform the process of claim 15, may comprise a computer).

11. Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (hereinafter Ho) (US Patent 12456068), in view of Rubio et al. (hereinafter Rubio) (“Quantum sensing networks for the estimation of linear functions”), further in view of Ashrafi et al. (hereinafter Ashrafi) (US PG-PUB 20190114557).
Regarding Claim 5, Ho in view of Rubio teaches the process of claim 1.
Ho in view of Rubio does not explicitly disclose wherein the multiple analytic functions are nonlinear combinations of the set of unknown parameters.
However, Ashrafi teaches wherein the multiple analytic functions are nonlinear combinations of the set of unknown parameters (Ashrafi, Par. [0162], “The application of the above described application of nonlinear modeling and forecasting to AI may be implemented according to two specific proposals. One is to use a Qubit neural network model based on 2-dimensional Qubits and the second is to expand the Qubits to multi-dimensional Qudits and further investigate their characteristic features, such as the effects of quantum superposition and probabilistic interpretation in the way of applying quantum computing to a neural network.”, therefore, the multiple functions (See Par. [0117-0118] for explicit recitation of functions) may comprise nonlinear combinations/modeling, as applied to a quantum neural network).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of claim 1, as disclosed by Ho in view of Rubio to include wherein the multiple analytic functions are nonlinear combinations of the set of unknown parameters, as disclosed by Ashrafi. One of ordinary skill in the art would have been motivated to make this modification to improve efficiency of quantum computing systems and enlarge its possibility of practical applications, through the modeling of nonlinear functions (Ashrafi, Par. [0162], “In recent years, scientists have developed quantum-neuro computing in which the algorithm of quantum computation is used to improve the efficiency of neural computing systems. The quantum state and the operator of quantum computation are both important to realize parallelisms and plasticity respectively in information processing systems. The complex valued representation of these quantum concepts allows neural computation system to advance in learning abilities and to enlarge its possibility of practical applications.”).

Regarding Claim 13, Ho in view of Rubio teaches the quantum sensor network of claim 7.
Ho in view of Rubio does not explicitly disclose wherein the multiple functions are nonlinear combinations of the set of unknown parameters.
However, Ashrafi teaches wherein the multiple functions are nonlinear combinations of the set of unknown parameters (Ashrafi, Par. [0162], “The application of the above described application of nonlinear modeling and forecasting to AI may be implemented according to two specific proposals. One is to use a Qubit neural network model based on 2-dimensional Qubits and the second is to expand the Qubits to multi-dimensional Qudits and further investigate their characteristic features, such as the effects of quantum superposition and probabilistic interpretation in the way of applying quantum computing to a neural network.”, therefore, the multiple functions (See Par. [0117-0118] for explicit recitation of functions) may comprise nonlinear combinations/modeling, as applied to a quantum neural network).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network of claim 7, as disclosed by Ho in view of Rubio to include wherein the multiple analytic functions are nonlinear combinations of the set of unknown parameters, as disclosed by Ashrafi. One of ordinary skill in the art would have been motivated to make this modification to improve efficiency of quantum computing systems and enlarge its possibility of practical applications, through the modeling of nonlinear functions (Ashrafi, Par. [0162], “In recent years, scientists have developed quantum-neuro computing in which the algorithm of quantum computation is used to improve the efficiency of neural computing systems. The quantum state and the operator of quantum computation are both important to realize parallelisms and plasticity respectively in information processing systems. The complex valued representation of these quantum concepts allows neural computation system to advance in learning abilities and to enlarge its possibility of practical applications.”).

12. Claims 8-10 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (hereinafter Ho) (US Patent 12456068), in view of Rubio et al. (hereinafter Rubio) (“Quantum sensing networks for the estimation of linear functions”), further in view of Zhang et al. (hereinafter Zhang) (US PG-PUB 20240142559).
Regarding Claim 8, Ho in view of Rubio teaches the quantum sensor network of claim 7.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a linear array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a linear array (Zhang, Par. [0030], “The sensor system 100 includes a quantum circuit 110 and a spatially distributed network 120 of M RF-photonic sensors 130, where M is an integer greater than one. Each RF-photonic sensor 130 is also referred to herein as a “node” of the network 120. In the example of FIG. 1 , the RF-photonic sensors 130 are equally spaced in a line, thereby forming a one-dimensional linear array.”, therefore, the plurality of quantum sensors is arranged in a linear array).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network of claim 7, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a linear array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a linear array, which may be more efficient and useful for measuring a one-dimensional angle-of-arrival of a received signal (Zhang, Par. [0030], “ In the example of FIG. 1 , the RF-photonic sensors 130 are equally spaced in a line, thereby forming a one-dimensional linear array. This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC).”).

Regarding Claim 9, Ho in view of Rubio teaches the quantum sensor network of claim 7.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a two-dimensional array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a two-dimensional array (Zhang, Par. [0030], “Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”, therefore, the plurality of quantum sensors may be arranged in a two-dimensional array)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network of claim 7, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a two-dimensional array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a two-dimensional array, which may be more efficient and useful for measuring a two-dimensional angle-of-arrival of a received signal (Zhang, Par. [0030], “This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC). Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”)

Regarding Claim 10, Ho in view of Rubio teaches the quantum sensor network of claim 7.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a three-dimensional array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a three-dimensional array (Zhang, Par. [0030], “Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”, therefore, the plurality of quantum sensors may be arranged in a three-dimensional array)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the quantum sensor network of claim 7, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a three-dimensional array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a three-dimensional array, which may be more efficient and useful for measuring a three-dimensional angle-of-arrival of a received signal (Zhang, Par. [0030], “This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC). Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”).

Regarding Claim 16, Ho in view of Rubio teaches the process of claim 15.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a linear array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a linear array (Zhang, Par. [0030], “The sensor system 100 includes a quantum circuit 110 and a spatially distributed network 120 of M RF-photonic sensors 130, where M is an integer greater than one. Each RF-photonic sensor 130 is also referred to herein as a “node” of the network 120. In the example of FIG. 1 , the RF-photonic sensors 130 are equally spaced in a line, thereby forming a one-dimensional linear array.”, therefore, the plurality of quantum sensors is arranged in a linear array).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of claim 15, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a linear array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a linear array, which may be more efficient and useful for measuring the angle-of-arrival of the received signal (Zhang, Par. [0030], “ In the example of FIG. 1 , the RF-photonic sensors 130 are equally spaced in a line, thereby forming a one-dimensional linear array. This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC).”).

Regarding Claim 17, Ho in view of Rubio teaches the process of claim 15.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a two-dimensional array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a two-dimensional array (Zhang, Par. [0030], “Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”, therefore, the plurality of quantum sensors may be arranged in a two-dimensional array)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of claim 15, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a two-dimensional array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a two-dimensional array, which may be more efficient and useful for measuring a two-dimensional angle-of-arrival of a received signal (Zhang, Par. [0030], “This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC). Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”)

Regarding Claim 18, Ho in view of Rubio teaches the process of claim 15.
Ho in view of Rubio does not explicitly disclose wherein the plurality of quantum sensors is arranged in a three-dimensional array.
However, Zhang teaches wherein the plurality of quantum sensors is arranged in a three-dimensional array (Zhang, Par. [0030], “Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”, therefore, the plurality of quantum sensors may be arranged in a three-dimensional array)

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of claim 15, as disclosed by Ho in view of Rubio to include wherein the plurality of quantum sensors is arranged in a three-dimensional array, as disclosed by Zhang. One of ordinary skill in the art would have been motivated to make this modification to enable the plurality of quantum sensors to be arranged in a three-dimensional array, which may be more efficient and useful for measuring a three-dimensional angle-of-arrival of a received signal (Zhang, Par. [0030], “This geometry is particularly useful for measuring the angle-of-arrival of the RF signal 180. A spacing of the linear array (i.e., a nearest-neighbor distance between pairs of the RF-photonic sensors 130) may be as large as several kilometers, or more. However, the spacing may alternatively be smaller, such as a few centimeters, or less. For example, some or all of the RF-photonic sensors 130 may be implemented on a single photonic integrated chip (PIC). Without departing from the scope hereof, the RF-photonic sensors 130 may be alternatively arranged as a different type of array, such as a linear array with unequal spacings, a loop (e.g., circle, square), a two-dimensional array, or a three-dimensional array. For detecting an angle-of-arrival, the spacing may be less than one-half of the wavelength of the RF signal 180.”).

13. Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Ho et al. (hereinafter Ho) (US Patent 12456068), in view of Rubio et al. (hereinafter Rubio) (“Quantum sensing networks for the estimation of linear functions”), further in view of Gao et al. (hereinafter Gao) (“Multi-hop teleportation in a quantum network based on mesh topology”)
Regarding Claim 20, Ho in view of Rubio teaches the process of claim 15.
Ho in view of Rubio does not explicitly disclose wherein the network topology is a star topology, a ring topology, or a mesh topology.
However, Gao teaches wherein the network topology is a star topology, a ring topology, or a mesh topology (Gao, Pg. 1, “In this paper, we propose a scheme of multi-hop teleportation in a quantum network based on mesh topology, which enables quantum teleportation to be realized between two nodes without a direct quantum channel.”, therefore, the quantum network topology is a mesh topology).

It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of claim 15, as disclosed by Ho in view of Rubio to include wherein the network topology is a star topology, a ring topology, or a mesh topology, as disclosed by Gao. One of ordinary skill in the art would have been motivated to make this modification to enable the use of a mesh topology, which connects all nodes to one another, allowing for transmissions to be distributed and hence providing a more robust and reliable quantum sensor network (Gao, Pg. 1, “Mesh topology is a network setup where nodes are connected to one another; it allows most transmissions to be distributed even when one of the connections fails [1, 2]. Specifically, mesh topology is dynamically self-organized and self-configured; mesh connectivity in the network can be automatically established and maintained by the nodes. Mesh networks have many advantages, they have low up-front costs, are easy to maintain, robust, and provide reliable service coverage. However, there are few existing studies [3, 4] on multi-hop teleportation in quantum networks based on mesh topology.”).

Conclusion
14. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is [phone]. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at [phone]. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123

Prosecution Timeline

• Apr 18, 2023: Application Filed
• Jan 08, 2026: Non-Final Rejection mailed — §103
• Mar 04, 2026: Response Filed
• May 26, 2026: Non-Final Rejection (signed) — §103
• Jun 05, 2026: Final Rejection mailed — §103 (current)