DETAILED ACTION
This action is pursuant to the claims filed on 03/04/2026. Claims 1-7, 9, and 11-21 are pending. A first action on the merits of claims 1-7, 9, and 11-21 is as follows.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/04/2026 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 9, 11, 13-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians (U.S. PGPub No. 2019/0328311) in view of Melamed (U.S. PGPub No. 2021/0133954), and in further view of Nachaliel (U.S. PGPub No. 2002/0183645).
Regarding claim 1, Mamigonians teaches A cancer detection system comprising: an electrode array configured to be worn by a user in contact with skin of the user, the electrode array including a plurality of electrodes (Fig 5, electrodes 411-413, etc. and electrodes 501-502, etc.); a power source (Fig 9 power supply 904); a signal generator configured to apply probe electrical signals to one or more of the plurality electrodes using the power source (Fig 9 energizing circuit 903); a detector configured to detect response electrical signals at one or more of the plurality of electrodes ([0038] monitoring circuit) and to generate digital signal outputs ([0061] processor 902 produces digital representation of monitored signal), the response electrical signals being responsive to the probe electrical signals and the digital signal outputs being representative of a physiological state of a tissue of the user ([0031-0032] energizing and monitoring and digital signal represents detection, or lack thereof, of tissue irregularities); control logic configured to activate the signal generator to generate a series of the probe electrical signals over a period of time ([0070-0077]), each of the probe electrical signals resulting in at least one of the response electrical signals ([0070-0077] each energizing signal has corresponding monitoring signal); memory configured to store the digital signal outputs ([0061] disclosing storing digital signals locally); the physiological state being indicative of cancer (device programmed to detect irregularities (i.e., lumps) of breast tissue which are indicative of cancer); an 1/O configured to communicate the digital signal output ([0061] disclosing uploading digital signal via data-output port).
Mamigonians fails to teach trained machine learning logic configured to detect a physiological state of the user based on the digital signal outputs, the physiological state being indicative of cancer.
In related prior art, Melamed teaches a similar device comprising trained machine learning logic configured to detect a physiological state of the user based on the digital signal outputs, the physiological state being indicative of cancer ([0037-0038 & 0041] machine learning models for computing likelihoods of breast cancer). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the processor and I/O of Mamigonians to incorporate the trained machine learning logic coupled with the I/O to detect a physiological state of the digital signal outputs indicative of cancer 1. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]).
Mamigonians fails to teach one electrode of the plurality of electrodes being a ring electrode having a hole sized to accept a nipple.
In related prior art, Nachaliel teaches a similar cancer detection system comprising a ring electrode having a hole sized to accept a nipple ([0108]) and further teaches measurement of the nipple provides lower impedance relative to other surfaces of the breast and providing a ring electrode to receive the nipple reduces the pressing of the nipple into the breast ([0108]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Mamigonians in view of Melamed and Nachaliel to incorporate one ring electrode having a hole sized to receive a nipple to arrive at claim 1. Doing so would advantageously enable the device to gather signals from a location of the breast having lower impedance than other surfaces of the breast ([0106]) while also avoiding pressing the nipple into the breast to minimize patient discomfort ([0108]).
Regarding claims 2-3, in view of the combination of claim 1 above, Mamigonians further teaches using breast structure data and the breast structure data includes electrostatic models of at least one type of cancer tissue and one type of non-cancerous tissue (Fig 6 and [0049] device uses electrostatic models to identify irregularities of breast tissue).
Mamigonians fails to teach the trained machine learning logic trained using said data; wherein the trained machine learning logic is configured to distinguish between cancerous breast tissue and non-cancerous breast tissue, based on the digital signal outputs.
In related prior art, Melamed teaches a similar device comprising trained machine learning logic configured to detect a physiological state of the user based on the digital signal outputs, the physiological state being indicative of cancer ([0037-0038 & 0041] machine learning models for computing likelihoods of breast cancer); wherein the trained machine learning logic is configured to distinguish between cancerous breast tissue and non-cancerous breast tissue, based on the digital signal outputs ([0037-0038 & 0041], machine learning logic for determining likelihood of breast cancer necessarily distinguishes between cancerous and non-cancerous tissue). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the processor and I/O of Mamigonians to incorporate the trained machine learning logic trained on electrostatic models of cancer and non-cancerous breast tissue to distinguish between cancerous and non-cancerous breast tissue to arrive at claims 2-3. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]).
Regarding claims 9, in view of the combination of claim 1 above, an alternative embodiment of Mamigonians teaches a connector capable of detachably connecting the electrode array to a bra (Figs 1-2 and [0032-0033] dome shaped substrates 204/205 are detachably connected to a bra 201/206). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mamigonians in view of Figs 1-2 of Mamigonians to incorporate the connector capable of detachably connecting the electrode array to a bra to arrive at claim 9. Doing so would advantageously enable the device to be deployed by a user themselves for continuous monitoring during user’s normal every-day life, rather than in a clinical setting.
Regarding claim 11, in view of the combination of claim 1 above, Mamigonians teaches wherein at least one electrode of the electrode array is configured to detect response electrical signals indicative of impedance through a nipple, areola or lactiferous duct (Fig 15, [0083] [0087] both disclosing response electrical signals are indicative of an impedance through breast tissue; i.e., response voltage 1504 generated from an inputted probe signal is directly indicative of tissue impedance).
Regarding claim 13, in view of the combination of claim 1 above, Mamigonians teaches wherein the electrode array is configured to be distributed in two cups of a bra or two bra inserts (See Fig 1 and right dome substrate 204 and left dome substrate 205 of Fig 2), and the detector is further configured to generate digital signal outputs that can be used to distinguish between response electrical signals generated from first and second breasts ([0048] disclosing comparing detection results from similar locations of each breast).
Regarding claim 14, in view of the combination of claim 13 above, Mamigonians teaches wherein the bra or the bra inserts include the electrode array, at least part of the power source, at least part of the signal generator and at least part of the detector (Fig 9, power source 904, energizing circuit 903, monitoring circuit are included in the system of Fig 1).
Regarding claim 15, in view of the combination of claim 1 above, Mamigonians teaches wherein the trained machine learning logic is configured to detect changes in the series of digital signal outputs over the period of time, of at least one month wherein the changes are indicative of a change in the physiological state of the user that is indicative of cancer ([0047] disclosing making comparisons of similar positions over time; see also claim 17 of the pgpub; furthermore, device is programmed to detect irregularities (i.e., lumps) of breast tissue which are indicative of cancer). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the processor of Mamigonians in view of Melamed to incorporate the trained machine learning logic configured to detect changes in the series of digital signal outputs over time of at least a month to detect cancer to arrive at claim 15. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]). Furthermore, comparing medical results over a period of time of at least a month is well-known in the art to yield the predictable result of identifying new and medically relevant changes to a patient’s body relative to a prior baseline.
Regarding claim 16, in view of the combination of claim 1 above, Mamigonians teaches wherein the trained machine learning logic is configured to compare digital signal outputs generated from members of the plurality of electrodes in contact with a right breast to digital signal outputs generated from members of the plurality of electrodes in contact with a left breast ([0048]).
Regarding claim 18, in view of the combination of claim 1 above, Mamigonians teaches wherein the machine learning logic is configured to compare the digital signal outputs to user specific baseline signals, wherein the user specific baseline signals are time dependent ([0047] disclosing making comparisons of similar positions over time such that the past signals are time dependent baseline signals; see also claim 17 of the pgpub). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the processor of Mamigonians in view of Melamed to incorporate the trained machine learning logic configured to compare the digital signal outputs to user specific time dependent baseline signals to arrive at claim 18. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]).
Regarding claim 19, in view of the combination of claim 1 above, Mamigonians teaches wherein the trained machine learning logic is configured to detect the changes indicative in the physiological state based on contralateral digital signal outputs from a first breast and a second breast ([0048]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the processor of Mamigonians in view of Melamed to incorporate the trained machine learning logic configured to detect changes based on contralateral digital signal outputs from a first breast and a second breast to arrive at claim 19. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]).
Regarding claim 20, in view of the combination of claim 1 above, Mamigonians teaches preprocessing logic configured to process the digital signal outputs (processor 902 comprises preprocessing logic to process the digital signal outputs), the processing of the digital signal outputs including: classifying the digital signal outputs by signal frequency, or normalizing the digital signal outputs as a function of position of the electrode array (Fig 6 and [0046-0049] digital signal outputs are normalized by position in electrode array to enable the comparison between different positions).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel, and in view of Stoval (U.S. PGPub No. 2019/0189264).
Regarding claim 4, in view of the combination of claim 1 above, Mamigonians further teaches the device configured to generate electrostatic models of breasts based on known tissue characteristics and breast structure data (Fig 6 and [0049] breast is modeled via measured characteristics).
Mamigonians fails to teach modeling logic configured to generate electrostatic models of breasts based on known tissue characteristics and breast structure data,wherein the tissue characteristics include characteristics of cancer tissue and a least two of: areola tissue, adipose tissue, cysts, calcifications, hypodermal fat, lactiferous ducts,and smooth muscle tissue; and training logic configured to train the machine learning logic to detect the cancer tissue based on the electrostatic models and simulations of impedance measurements of one or two breasts as measured by the electrode array.
Melamed further teaches modeling logic ([0035]) and training logic configured to train the machine learning logic to detect cancer tissue ([0006-0008], [0023]).
In related prior art, Stoval teaches modeling logic configured to generate electrostatic models of breasts based on known tissue characteristics and breast structure data, wherein the tissue characteristics include characteristics of cancer tissue ([0046] classification 6) and a least two of: areola tissue, adipose tissue, cysts ([0046] classification 2), calcifications, hypodermal fat, lactiferous ducts, and smooth muscle tissue ([0046] classification 1 is indicative of ‘normal’ tissue, e.g., areola tissue, adipose tissue, smooth muscle tissue, etc.); and training logic configured to train the machine learning logic to detect the cancer tissue based on the electrostatic models and simulations of impedance measurements of one or two breasts as measured by the electrode array ([0046] machine learning AI is trained based on deep learning methodology to classify images). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mamigonians in view of Melamed, Nachaliel and Stoval to incorporate the modeling logic to generate electrostatic models of breasts and training logic to train the machine learning logic to detect cancer tissue based on the electrostatic models and impedance simulations to arrive at claim 4. Doing so would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses ([0037-0038, 0041]).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel, and in view of Cantu (U.S. PGPub No. 2020/0069191).
Regarding claim 5, in view of the combination of claim 1 above,
Mamigonians fails to teach a surface sensor configured to detect temperature and/or humidity and wherein the trained machine learning logic is further configured to detect the changes based on data generated using the surface sensor.
In related prior art, Cantu teaches a similar device with a surface sensor configured to detect temperature and/or humidity (Fig 1 temperature sensing assemblies 130) and wherein the trained machine learning logic is further configured to detect the changes based on data generated using the surface sensor (Figs 5-6 [0015] [0029] disclosing machine learning logic to detect changes). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mamigonians in view of Melamed, Nachaliel and Cantu to incorporate the surface temperature sensor and the processor and trained machine learning logic to detect changes based on the temperature data to arrive at claim 5. Doing so would advantageously enable the system to detect changes indicative of a risk of breast cancer to provide better diagnostic or early detection capabilities ([0029]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel, and in view of Ramahi (U.S. PGPub No. 2019/0274617).
Regarding claim 6, in view of the combination of claim 1 above,
Mamigonians fails to teach an ultrasound system and wherein the trained machine learning logic is further configured to detect the physiological state based on data generated using the ultrasound system.
In related prior art, Ramahi teaches the use of an ultrasound system for detecting physiological state (e.g., cancer) based on data generated using the ultrasound ([0003] disclosing ultrasound scanning is among the most common imaging modalities for diagnosis and detection of breast cancer). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the device of Mamigonians in view of Melamed, Nachaliel and Ramahi to incorporate the ultrasound system such that the processor and machine learning logic is capable of detecting the physiological state based on the ultrasound data. Doing so would be obvious to one of ordinary skill in the art as the use of ultrasound machines to detect breast cancer is well-known in the art to yield predictable results therein ([0003]). Furthermore, incorporating the ultrasound data to be interpreted by machine learning logic would be obvious to one of ordinary skill in the art as the use of trained machine learning logic to assist with medical diagnosis is well-known in the art to yield the predictable result of providing preliminary medical diagnoses (Melamed [0037-0038, 0041]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel, and in view of McKenna (U.S. PGPub No. 2011/0245622).
Regarding claims 7, in view of the combination of claim 1 above,
Mamigonians fails to teach positioning logic configured to detect a position of the electrode array based on detection of electro-cardio signals.
In related prior art, McKenna teaches a similar device comprising positioning logic configured to detect a position of the electrode array based on detection of electro-cardio signals (Figs 7-8, and 15,[0037-0038] discloses ECG signals and sensor location classification algorithms based on sensed data to determine positions of sensors). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mamigonians in view of McKenna to incorporate the positioning logic configured to detect a position of the electrode array based on detect ECG signals to arrive at claim 7. Doing so would advantageously enable the device with the ability to self-determine the position of its electrode array to indicate whether the sensors are correctly or incorrectly applied to a user (see Fig 16, steps 140/144).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel, and in view of Davies (U.S. PGPub No. 2006/0241514).
Regarding claim 12, in view of the combination of claim 1 above, Mamigonians teaches detecting cancer based on breast irregularities ([0004 & 0031]).
Mamigonians fails to teach wherein the cancer includes Ductal Carcinoma In Situ (DCIS),Invasive Ductal Carcinoma (IDC), Invasive Lobular Carcinoma (ILC), Triple-Negative Breast Cancer, HER2-Positive Breast Cancer, or Inflammatory Breast Cancer (IBC).
In related prior art, Davies teaches wherein the cancer includes Ductal Carcinoma In Situ (DCIS),Invasive Ductal Carcinoma (IDC), Invasive Lobular Carcinoma (ILC), Triple-Negative Breast Cancer, HER2-Positive Breast Cancer, or Inflammatory Breast Cancer (IBC) ([0119] disclosing DCIS occurs when mass lesions forming within ducts). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mamigonians in view of Melamed, Nachaliel and Davies to incorporate the detecting of breast irregularities indicative of at least DCIS to arrive at claim 17. Doing so would advantageously enable the system to detect a breast cancer as it is well-known in the art that certain types of breast cancers are indicated by irregularities in the breast (Davies [0119]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel and in view of Gimzewski (U.S. PGPub No. 2017/0231499).
Regarding claim 17, in view of the combination of claim 1 above, Mamigonians teaches wherein the physiological state is further indicative of presence of non-cancerous tissue (tissue not identified as not irregular is non-cancerous tissue)
Mamigonians fails to teach including at least one of: cysts, calcifications and adenomas.
In related prior art, Gimzewski teaches wherein irregularities of breast tissue may be cancers, cysts, and lipomas ([0041]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the device of Mamigonians in view of Melamed, Nachaliel and Gimzewski to incorporate the physiological state of tissue including at least one of cysts, calcifications, and adenomas to arrive at claim 17. Doing so would advantageously enable the device to differentiate from cancerous and non-cancerous tissues ([0041]).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, Nachaliel and in view of Moreno (WO 2020/201546).
Regarding claim 21, in view of the combination of claim 1 above,
Mamigonians fails to teach wherein each of the electrodes of the plurality of electrodes, except for the one electrode, are spot electrodes characterized by being round but not ring-shaped.
In related prior art, Moreno teaches a similar cancer detection system comprising: an electrode array configured to be worn by a user in contact with skin of the user (Figs 3-4, bra 10 with cups 1 and 11 including array of point electrodes 7), wherein each of the electrodes of the plurality of electrodes are spot electrodes characterized by being round but not ring-shaped (Fig 5 point electrodes 7 disclosed as round on Pg 7 of the translation). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electrodes of Mamigonians in view of Moreno to incorporate the plurality of electrodes as round spot electrodes to arrive at claim 21. Doing so would have been obvious to one of ordinary skill in the art as a simple substitution of one well-known electrode configuration (Mamigonians, Fig 5 ring electrodes 411, 412, etc.) for another well-known electrode configuration (Moreno Fig 3, point electrodes 7 arranged in sets of circles 71) to yield the predictable result of providing an impedance electrode array for monitoring for cancerous tissue within breast tissue.
Double Patenting
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.
Claims 1-7, 9, and 11-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 41-60 of copending Application No. 19/196,572 in view of Nachaliel.
This is a provisional nonstatutory double patenting rejection.
Regarding claim 1, the reference application recites all of the limitations of instant claim 1 except for one electrode of the plurality of electrodes being a ring electrode having a hole sized to accept a nipple.
In related prior art, Nachaliel teaches a similar cancer detection system comprising a ring electrode having a hole sized to accept a nipple ([0108]) and further teaches measurement of the nipple provides lower impedance relative to other surfaces of the breast and providing a ring electrode to receive the nipple reduces the pressing of the nipple into the breast ([0108]). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the device of Mamigonians in view of Melamed and Nachaliel to incorporate one ring electrode having a hole sized to receive a nipple to arrive at claim 1. Doing so would advantageously enable the device to gather signals from a location of the breast having lower impedance than other surfaces of the breast ([0106]) while also avoiding pressing the nipple into the breast to minimize patient discomfort ([0108]).
Regarding claims 2-6, each of reference claims 42-46 respectively correspond to instant claims 2-6.
Regarding claim 7, reference claim 47 anticipates instant claim 7.
Regarding claims 9, and 11-20, reference claims 49 and 51-60 respectively correspond to instant claims 9 and 11-20.
Claim 21 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 41-60 of copending Application No. 19/196,572 in view of Nachaliel and Moreno (WO 2020/201546).
This is a provisional nonstatutory double patenting rejection.
Regarding claim 21, in view of the combination of claim 1 above,
The reference claims fails to teach wherein each of the electrodes of the plurality of electrodes, except for the one electrode, are spot electrodes characterized by being round but not ring-shaped.
In related prior art, Moreno teaches a similar cancer detection system comprising: an electrode array configured to be worn by a user in contact with skin of the user (Figs 3-4, bra 10 with cups 1 and 11 including array of point electrodes 7), wherein each of the electrodes of the plurality of electrodes are spot electrodes characterized by being round but not ring-shaped (Fig 5 point electrodes 7 disclosed as round on Pg 7 of the translation). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the electrodes of the reference claims in view of Moreno to incorporate the plurality of electrodes as round spot electrodes to arrive at claim 21. Doing so would have been obvious to one of ordinary skill in the art as a simple substitution of one well-known electrode configuration (electrodes of reference claims) for another well-known electrode configuration (Moreno Fig 3, point electrodes 7 arranged in sets of circles 71) to yield the predictable result of providing an impedance electrode array for monitoring for cancerous tissue within breast tissue.
Response to Arguments
Applicant’s arguments, see remarks, filed 03/04/2026, with respect to the rejection(s) of claim(s) 1-7, 9, and 11-20 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in further view of the Nachaliel reference.
Conclusion
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/ADAM Z MINCHELLA/Primary Examiner, Art Unit 3794