DETAILED ACTION
This action is in response to the restriction/election filed on May 11, 2026. Invention I (claims 1-10) has been elected without traverse. Invention II (claims 11-20) has been withdrawn from consideration. Claims 1-10 have been examined and are currently pending.
Notice of Pre-AIA or AIA Status
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 Restriction
Applicant’s election without traverse of Invention I in the reply filed on May 11, 2026 is acknowledged.
Inventorship
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Information Disclosure Statement
The Information Disclosure Statements filed May 7, 2025 and May 15, 2026 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Objections
Claim 4 is objected to because of the following informalities: Dependent claim 4 recites the term, “the edge” in line 2 lacks antecedent basis. Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2 and 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Larsson US Publication 20050059928 A1 in view of Gordon et al. US Publication 20240156394 A1.
Claim 1:
As per claim 1, Larsson teaches a system comprising:
an optical sensor configured to attach to a user's breast and generate data based on optical signals that are outputted by the optical sensor (paragraphs 0007, 0009, 0013, and 0030 “Accordingly, various measurement methods are possible, for example, ultrasound, detection and measurement of electrical activity which records, for example, resistance and impedance between two spaced areas of the breast, and so on. Electrodes are placed on the breast skin for measuring electric signals, optical sensors for detecting and/or measuring, for example, light absorption or reflection, and acoustic sensors for detecting and/or measuring ultrasound. It will be understood that the sensors contemplated by the present invention can be conventional sensors, and the like, designed for detecting various changes in humans and, in particular, human breast tissue, including the skin. Other sensors may also be employed to study other phenomenon associated with breast pumping.” and “An aspect of the present invention includes a breast shield for use on a human breast including a breast-receiving portion, sized and shaped to receive a nipple and at least some surrounding breast. One or more sensors are connected to the breast-receiving portion and capable of sensing changes in the human breast. Alternate embodiments of this aspect of the present invention include a sensor being one or more of an optical sensor, an electrode, a thermal sensor and an acoustic sensor. The optical sensor may be adapted to detect changes in light through or from the breast. The electrode may be a pair of electrode parts, a first of which is used to apply voltage to the breast and a second of which is used to receive current conducted from the first of the pair of electrode parts. The acoustic sensor may be adapted to detect changes in breast tissue, such as density or shape.”);
Larsson does not teach and a mobile device in network communication with the optical sensor, wherein the mobile device is configured to: However, Gordon teaches Breastfeeding Monitor and Smart Insight System and further teaches, “For example, mobile device 110 may be implemented as a smart phone, a tablet, a laptop computer, a desktop computer, a music storage and playback device, a personal digital assistant, or any other device capable of implementing a software application that may interact with, control, and provide information from sensor clip 105… While examples herein use a smart phone as an exemplary mobile device 110 for controlling, interacting with, and receiving information from sensor clip 105, any device capable of executing an application program may be similarly used in place of a smart phone.” (paragraph 0018), “Mobile device 110 may interface with sensors of sensor clip 105 using, for example, a Bluetooth low energy radio connection. Accordingly, mobile device 110 may receive data in real-time from sensors and other components of sensor clip 105, such as sounds detected by a microphone and an accelerometer signal.” (paragraph 0019), and “In one embodiment, wireless communication controller 315 may include processor 350 and establish communication with mobile device 110 by wireless communication circuitry 125. Exemplary wireless communication circuitry 125 may be implemented using Bluetooth®, Wi-Fi, ZigBee, Z-Wave, RF4CE, cellular channels, or others that operate in accordance with protocols defined in IEEE (Institute of Electrical and Electronics Engineers) 802.11, 801.11a, 801.11b, 801.11e, 802.11g, 802.11h, 802.11i, 802.11n, 802.16, 802.16d, 802.16e, the standard formerly known as 802.15.1, or 802.16m using any network type including a wide-area network (“WAN”), a local-area network (“LAN”), a 2G network, a 3G network, a 4G network, a 5G network, a Worldwide Interoperability for Microwave Access (WiMAX) network, a Long Term Evolution (LTE) network, Code-Division Multiple Access (CDMA) network, Wideband CDMA (WCDMA) network, any type of satellite or cellular network, or any other appropriate protocol to facilitate communication between sensor clip 305 and mobile device 110 known to one of ordinary skill in the art.” (paragraph 0032). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include and a mobile device in network communication with the optical sensor, wherein the mobile device is configured to as taught by Gordon in order to exchange or transmit/receive information or data.
Larsson does not teach receive the data from the optical sensor. However, Gordon teaches Breastfeeding Monitor and Smart Insight System and further teaches, “A system is disclosed which includes a sensor clip, having at least one sensor, and a mobile device. The mobile device wirelessly receives sensor data from the sensor clip sensor.” (paragraph 0006), “For example, mobile device 110 may be implemented as a smart phone, a tablet, a laptop computer, a desktop computer, a music storage and playback device, a personal digital assistant, or any other device capable of implementing a software application that may interact with, control, and provide information from sensor clip 105… While examples herein use a smart phone as an exemplary mobile device 110 for controlling, interacting with, and receiving information from sensor clip 105, any device capable of executing an application program may be similarly used in place of a smart phone.” (paragraph 0018), and “Mobile device 110 may interface with sensors of sensor clip 105 using, for example, a Bluetooth low energy radio connection. Accordingly, mobile device 110 may receive data in real-time from sensors and other components of sensor clip 105, such as sounds detected by a microphone and an accelerometer signal.” (paragraph 0019). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include receive the data from the optical sensor as taught by Gordon in order to in order to analyze the data received.
Larsson does not teach process the data based on applying locally-deployed models to the data. However, Gordon teaches Breastfeeding Monitor and Smart Insight System and further teaches, “Server 120 may analyze data received from mobile device 110. For example, server 120 may use machine learning and/or other artificial intelligence tools to identify an amount of milk consumed by a child during a breastfeeding session based on sound data, milk production data, and any other data collected by either sensor clip 305 or mobile device 110. Additionally, server 120 may utilize a variety of data sources to produce information useful to a mother throughout the breastfeeding period. For example, server 120 may compare data collected by sensor clip 305 and data generated by server 120 to provide further analysis using data such as data input by a user, such as the age, sex, weight, and other parameters of a baby and the age, weight, breast size, and other parameters of a mother…” (paragraph 0038). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include process the data based on applying locally-deployed models to the data as taught by Gordon in order to gain insights about the data received.
Larsson does not teach generate, based on applying the locally-deployed models to the processed data, insights about a breastmilk supply. However, Gordon teaches Breastfeeding Monitor and Smart Insight System and further teaches, “Smart insights 405 may provide information and suggestions to a mother. For example, smart insights 405 may include information such as a feeding session volume in real-time and total, an average feeding session volume over a given time, and a baby's growth and development progress through a variety of parameters. Additionally, smart insights 405 may include suggestions such as how a mother may manage milk production such as consuming more water, when to consume water, when to feed, etc. In this manner, a mother may gain the benefit of the recorded data, artificial intelligence computing, such as machine learning, and aggregated data over the course of breastfeeding to avoid any detrimental effects to, or slowed growth of, her baby. For example, a mother need not be alerted to her baby's lack of milk intake by such detrimental effects as slow weight gain, concentrated urine, and dark, dry stools.” (paragraph 0040) and “Server 120 may analyze data received from mobile device 110. For example, server 120 may use machine learning and/or other artificial intelligence tools to identify an amount of milk consumed by a child during a breastfeeding session based on sound data, milk production data, and any other data collected by either sensor clip 305 or mobile device 110…” (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include generate, based on applying the locally-deployed models to the processed data, insights about a breastmilk supply as taught by Gordon in order to provide recommendations or useful tips to improve the milk supply.
Larsson does not teach and return output for presentation in a graphical user interface (GUI) display, wherein the output is based on the processed data and the generated insights. However, Gordon teaches Breastfeeding Monitor and Smart Insight System and further teaches, “Server 120 may analyze data received from mobile device 110. For example, server 120 may use machine learning and/or other artificial intelligence tools to identify an amount of milk consumed by a child during a breastfeeding session based on sound data, milk production data, and any other data collected by either sensor clip 305 or mobile device 110. Additionally, server 120 may utilize a variety of data sources to produce information useful to a mother throughout the breastfeeding period. For example, server 120 may compare data collected by sensor clip 305 and data generated by server 120 to provide further analysis using data such as data input by a user, such as the age, sex, weight, and other parameters of a baby and the age, weight, breast size, and other parameters of a mother. Additionally, data from medical authorities, such as suggested milk intake amounts for a baby at a given age, suggested water intake and nutrition of mother for optimal milk production and nutritional value, etc. may be used by server 120 to provide useful information. This information may then be communicated back to mobile device 110 for display on smartphone application 115 in the form of smart insights 405.” (paragraph 0039), “Smart insights 405 may provide information and suggestions to a mother. For example, smart insights 405 may include information such as a feeding session volume in real-time and total, an average feeding session volume over a given time, and a baby's growth and development progress through a variety of parameters. Additionally, smart insights 405 may include suggestions such as how a mother may manage milk production such as consuming more water, when to consume water, when to feed, etc. In this manner, a mother may gain the benefit of the recorded data, artificial intelligence computing, such as machine learning, and aggregated data over the course of breastfeeding to avoid any detrimental effects to, or slowed growth of, her baby. For example, a mother need not be alerted to her baby's lack of milk intake by such detrimental effects as slow weight gain, concentrated urine, and dark, dry stools.” (paragraph 0040), and “A software application, for example, smartphone application 115, operating on mobile device 110 may provide further analysis of sensor data received from sensor clip 105 for display on mobile device 110. For example, a processor associated with mobile device 110 may analyze data received from sensor clip 105 to calculate and display a volume of milk consumed per feeding session based on interaction with server 120. Additionally, a processor executing smartphone application 115 may receive data analyzed by server 120 for display on mobile device 110. For example, mobile device 110 may send sensor data from sensor clip 105 to server 120 to display a calculated volume of milk consumed per feeding session as discussed in further detail below, or may itself multiply the number of detected suckling and swallowing sounds by an average volume of milk expressed per swallow or per suckle to obtain a volume of milk consumed in real time and per feeding session.” (paragraph 0020). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include and return output for presentation in a graphical user interface (GUI) display, wherein the output is based on the processed data and the generated insights as taught by Gordon in order to provide the end user recommendations or useful tips for display.
Claim 2:
As per claim 2, Larsson and Gordon teach the system of claim 1 as described above and Larsson further teaches wherein the optical sensor comprises:
one or more light emitters that are configured to output the optical signals, and one or more light detectors that are configured to detect the outputted optical signals to generate the data (paragraph 0034).
Claim 4:
As per claim 4, Larsson and Gordon teach the system of claim 1 as described above and Gordon further teaches wherein processing the data comprises, locally on the edge, determining the breastmilk supply over one or more periods of time (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include wherein processing the data comprises, locally on the edge, determining the breastmilk supply over one or more periods of time as taught by Gordon in order to maintain and store records of milk supply.
Claim 5:
As per claim 5, Larsson and Gordon teach the system of claim 4 as described above and Gordon further teaches wherein the one or more periods of time comprise a past period of time, a current period of time, or a future period of time (paragraph 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include wherein the one or more periods of time comprise a past period of time, a current period of time, or a future period of time as taught by Gordon in order to classify the time periods for research purposes.
Claim 6:
As per claim 6, Larsson and Gordon teach the system of claim 4 as described above and Gordon further teaches wherein generating the insights comprises, locally on the edge:
identifying periods of time for breastfeeding or pumping (paragraphs 0020 and 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include identifying periods of time for breastfeeding or pumping as taught by Gordon in order to track milk supply.
and generating suggestions for improving habits or behaviors of the user (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include and generating suggestions for improving habits or behaviors of the user as taught by Gordon in order to assist in the milk supply or breast-feeding process.
Claim 7:
As per claim 7, Larsson and Gordon teach the system of claim 1 as described above and Gordon further teaches wherein returning the output comprises generating visualizations of the breastmilk supply over one or more periods of time (paragraphs 0020, 0039, and 0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include wherein returning the output comprises generating visualizations of the breastmilk supply over one or more periods of time as taught by Gordon in order to generate a graphical representation for easy viewing and/or analysis.
Claim 8:
As per claim 8, Larsson and Gordon teach the system of claim 1 as described above and Gordon further teaches wherein returning the output comprises generating indications corresponding to the insights (paragraphs 0020, 0039, and 0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include wherein returning the output comprises generating indications corresponding to the insights as taught by Gordon in order to provide an analysis for viewing.
Claim 9:
As per claim 9, Larsson and Gordon teach the system of claim 1 as described above and Gordon further teaches wherein the locally-deployed models comprise Al or NNs (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include wherein the locally-deployed models comprise Al or NNs as taught by Gordon in order to provide computer generated recommendations or results.
Claim 10:
As per claim 10, Larsson and Gordon teach the system of claim 1 as described above Gordon further teaches wherein the locally-deployed models were trained in a process that comprises:
collecting training data that includes optical signal measurements, milk volumes, and user health or biometrics data (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include collecting training data that includes optical signal measurements, milk volumes, and user health or biometrics data as taught by Gordon in order to generate datasets or parameters associated with a plurality of users.
processing the training data (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include processing the training data as taught by Gordon in order to analyze the data received.
defining variables of interest for modeling (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include defining variables of interest for modeling as taught by Gordon in order to in order to establish boundaries or parameters for further study or analysis.
selecting a pre-trained model (paragraph 0039). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include selecting a pre-trained model as taught by Gordon in order to generate the best results or data.
training the selected model based on extracted features in the processed training data and the variables of interest, wherein training the selected model further comprises correlating the extracted features with information about milk supply conditions (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include training the selected model based on extracted features in the processed training data and the variables of interest, wherein training the selected model further comprises correlating the extracted features with information about milk supply conditions as taught by Gordon in order to generate the best results or data.
iteratively improving the trained model until a desired threshold accuracy level is achieved (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include iteratively improving the trained model until a desired threshold accuracy level is achieved as taught by Gordon in order to generate the best results or data.
compressing the trained model for local edge deployment at the mobile device (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include compressing the trained model for local edge deployment at the mobile device as taught by Gordon in order to utilize or apply the model on a mobile device.
and returning the compressed model for runtime use at the mobile device (paragraphs 0039-0040). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to modify Larsson to include and returning the compressed model for runtime use at the mobile device as taught by Gordon in order to utilize or apply the model on a mobile device.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Larsson and Gordon as applied to claim 1 above, and further in view of Elhag et al. US Publication 20060069319 A1.
Claim 3:
As per claim 3, Larsson and Gordon teach the system of claim 1 as described above but do not teach wherein the optical signals comprise one or more wavelengths from a group consisting of: 550 nanometers (nm), 660 nm, and 880 nm. However, Elhag teaches a Monitoring Device, Method, and System and further teaches, “In a preferred embodiment, the optical sensor 30 is a photodetector 130 and a single light emitting diode ("LED") 135 transmitting light at a wavelength of approximately 660 nanometers…” (paragraph 0053). Therefore, it would have been obvious to one of ordinary skill in the art at time of filing to modify Larsson and Gordon to include wherein the optical signals comprise one or more wavelengths from a group consisting of: 550 nanometers (nm), 660 nm, and 880 nm as taught by Elhag in order to analyze data using different wavelengths.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
WO 2013093739 A1 A Method for Providing an Indication as to the Amount of Milk Remaining in a Breast during Lactation
A breast pump assembly (13) comprising:-a non-removably mounted optical sensor (3, 6, 7, 9, 10, 11, 12) configured for measuring an optical characteristic of milk that has just been expressed from a breast and has entered the breast pump (13);-a control unit configured for:-comparing an output signal of the optical sensor (3, 6, 7, 9, 10, 11, 12), representing said measured optical characteristic, with data representing a corresponding optical characteristic of a sample of milk having a known fat content to determine the fat content of said expressed milk; and-determining an indication of the amount of milk remaining in the breast from the determined fat content of said expressed milk.
Makower et al. US Publication 20170172485 A1 Systems, Devices and Methods for Assessing Milk Volume Expressed from a Breast
Makower discloses systems and methods for assessing milk volume changes within a breast are described. Also described are systems and methods for assessing volume changes in the stomach of an infant.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682
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