DETAILED ACTION
This action is pursuant to the claims filed on 02/09/2026. Claims 41-60 are pending. A final action on the merits of claims 41-60 is as follows.
Response to Amendment
Applicant’s amendment to the claims are acknowledged and entered accordingly.
Election/Restrictions
Applicant's election with traverse of the originally filed invention in the reply filed on 02-09/2026 is acknowledged. The traversal is on the ground(s) that “there was no undo burden on the Examiner to search and examine these claims”. This is not found persuasive because the applicant had attempted to switch the statutory classes of invention from a product to a method after a first action on the merits was received. As such, the prior claim set had acquired a separate status in the art in view of their different classification. The prior claim set had acquired a separate status in the art due to their recognized divergent subject matter. The prior claim set had required a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries).
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claims 42-60 are objected to because of the following informalities:
Each of dependent claims 42-60 erroneously refer to canceled claims, but are clearly meant to refer to pending claims (e.g., 1 = 41, 2 =42, 7 = 47, 13 = 53).
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 41-43, 47-51, 53-56, 58-60 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).
Regarding claim 41, 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); comparing 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); 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 and to compare the digital signal outputs to user specific time dependent baseline signals to arrive at claim 41. 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 42-43, in view of the combination of claim 41 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 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 47-49, in view of the combination of claim 41 above, Mamigonians teaches at least one positioning structure configured to position the electrode array on a breast (see Figs 5, device includes positioning structure to position the electrode array on a breast) or further including positioning logic configured to detect a position of the electrode array based on detection of electro-cardio signals; wherein the positioning structure is configured to position the electrode relative to an areola (see Figs 1-2); wherein the positioning structure includes a connection to a bra (see Fig 1 device is connected to a bra during use).
Regarding claim 50, in view of the combination of claim 41 above, Mamigonians teaches wherein at least one electrode of the electrode array is a ring electrode disposed around a positioning structure (Fig 5, electrode array includes ring electrode disposed around positioning structure).
Regarding claim 51, in view of the combination of claim 41 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 53, in view of the combination of claim 41 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 distinguish between response electrical signals generated from first and second breasts ([0048] disclosing comparing detection results from similar locations of each breast).
Regarding claim 54, in view of the combination of claim 43 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 55, in view of the combination of claim 41 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 55. 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 56, in view of the combination of claim 41 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 58, in view of the combination of claim 41 above, Mamigonians teaches wherein the trained machine learning logic is configured to detect physiological changes that occur in a first breast but not in a second breast ([0047-0048] disclosing detecting changes that can occur in one breast that don’t occur in a second breast). 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 that occur in a first breast, but not a second breast to arrive at claim 58. 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 59, in view of the combination of claim 41 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 59. 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 60, in view of the combination of claim 41 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) 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, and in view of Stoval (U.S. PGPub No. 2019/189264).
Regarding claim 44, in view of the combination of claim 41 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 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 44. 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) 45 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, and in view of Cantu (U.S. PGPub No. 2020/0069191).
Regarding claim 45, in view of the combination of claim 41 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 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 45. 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) 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, and in view of Ramahi (U.S. PGPub No. 2019/0274617).
Regarding claim 46, in view of the combination of claim 41 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 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) 52 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, and in view of Davies (U.S. PGPub No. 2006/0241514).
Regarding claim 52, in view of the combination of claim 41 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 and Davies to incorporate the detecting of breast irregularities indicative of at least DCIS to arrive at claim 52. 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) 57 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mamigonians in view of Melamed, and in view of Gimzewski (U.S. PGPub No. 2017/0231499).
Regarding claim 57, in view of the combination of claim 41 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 and Gimzewski to incorporate the physiological state of tissue including at least one of cysts, calcifications, and adenomas to arrive at claim 57. Doing so would advantageously enable the device to differentiate from cancerous and non-cancerous tissues ([0041]).
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 41-60 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-7, 9, and 11-21 of copending Application No. 19/246,240 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claims anticipate and/or make obvious the instant claims.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to Arguments
Applicant's arguments filed 02/09/2026 have been fully considered but they are not persuasive. Applicant states the arguments made in 19/246,240 on 11/21/2025 are incorporated into the instant remarks.
Applicant argues that it would not have been obvious to have modified Mamigonias in view of Malamed to incorporate a trained machine learning logic configured to detect a physiological state indicative of cancer based on the digital signal outputs.
These arguments are unpersuasive. Mamigonias explicitly teaches detecting response electrical signals from signals applied from an electrode array to detect physiological states indicative of breast cancer (see at least Figs 5, 9, and [0031-0032], [0061], [0070-0077]). Mamigonias discloses the necessary processing and circuitry capabilities to detect said physiological state indicative of cancer. While Mamigonias fails to explicitly disclose the use of a trained machine learning logic to assist in said detection, the technique of using trained machine learning logic is well-known in the art as evidenced by the Melamed reference disclosing trained machine learning logic used in identifying breast cancer disclosed in at least [0037-0038 & 0041]. The mere fact that Melamed does not utilize digitized impedance measurements is not particularly relevant when Mamigonias explicitly discloses using digitized impedance measurements to detect physiological states indicative of cancer. Melamed was relied upon for making it known that the use of trained machine learning logic is well-known in the art to assist in medical diagnosis to yield predictable results therein. As such, applicant’s arguments are unpersuasive.
Applicant’s arguments to remaining claims 42-60 are equally unpersuasive for the reasons stated above.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Adam Z Minchella whose telephone number is (571)272-8644. The examiner can normally be reached M-Fri 7-3 EST.
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/ADAM Z MINCHELLA/Primary Examiner, Art Unit 3794
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