DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 14 November 2025 has been entered.
Status of Claims
In the reply filed on 14 November 2025, the following changes have been made: amendments to claims 1, 8, and 20.
Claims 1-20 are currently pending and have been examined.
Notice to Applicant
The use of “optionally”, “and/or”, “at least one of”, other “optionally”-related synonyms, etc. in the claims are all interpreted as alternative limitations (MPEP 2173.05(h)(II)).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claims 1-7) and process (claims 8-20).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
a sensor or set of sensors which analyze a sample of human milk, and
an artificial intelligence server including a processor or several processors and an artificial intelligence engine which:
receives sensor data generated by the sensor or sensors;
analyzes the sensor data to identify concentrations of macronutrients and or micronutrients in the sample of human milk;
compares the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with nutritional guidelines for a particular infant;
compares the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with feeding protocols obtained from historical clinical data about one or more infants with similar clinical profiles to the particular infant;
identifies one or more disease risk scores based on clinical data specific to the particular infant; and
provides to a device a nutritional recommendation for optimal personalized milk fortification based on the comparison of the macronutrients and or micronutrients in the sample of human milk with chosen nutritional guidelines, and with feeding protocols obtained from historical clinical data about one or more infants with similar clinical profiles for the particular infant, and optionally disease risk scores.
Claim 8 recites steps of
receiving, by an artificial intelligence server which includes one or more processors, sensor data generated by a sensor or sensors;
analyzing, by the artificial intelligence server which includes one or more processors, the sensor data to identify concentrations of macronutrients and or micronutrients, contamination, and or freshness of a sample of human milk;
comparing, by the artificial intelligence server which includes one or more processors, the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with chosen nutritional guidelines;
comparing, by the artificial intelligence server which includes one or more processors, the concentrations of macronutrients and or micronutrients in the sample of human milk with feeding protocols obtained from historical clinical data about one or more infants from the analyzed sensor data with similar clinical profiles to the particular infant;
identifying, by the artificial intelligence server which includes one or more processors, one or more risk scores for a particular infant based on the likelihood of developing diseases affected by nutrition, including but not limited to growth faltering, bronchopulmonary dysplasia, necrotizing enterocolitis, and sepsis; and
providing to a device, by the artificial intelligence server which includes one or more processors, an optimized personalized nutritional recommendation for milk fortification, and optionally disease risk scores.
Claim 20 recites steps of
receiving, by an artificial intelligence server which includes one or more processors, sensor data generated by a sensor or more than one sensor;
analyzing, by the artificial intelligence server which includes one or more processors, the sensor or sensors data to identify concentrations of macronutrients and or micronutrients in a sample of human milk;
comparing, by the artificial intelligence server which includes one or more processors, the constituent elements of macronutrients and micronutrients in the sample of human milk from the analyzed sensor data with the chosen nutritional guidelines;
identifying, by the artificial intelligence server which includes one or more processors, one or more disease risk scores to a particular infant based on one or more clinical data associated with information about the particular infant and his or her parents; and
providing to a device, by the artificial intelligence server which includes one or more processors, an optimized nutritional recommendation for milk fortification.
These steps directed to analyzing data to provide a nutritional recommendation for optimal personalized milk fortification, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind but for recitation of generic computer components. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through performing determinations, including observations and evaluations, on milk nutrition. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1, 8, and 20, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as by
[Claim 1] a sensor or set of sensors; an artificial intelligence server including a processor or several processors and an artificial intelligence engine; generated by the sensor or sensors; and, to a device amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
[Claim 8] by the artificial intelligence server which includes one or more processors; generated by a sensor or sensors; and, to a device amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
[Claim 20] by the artificial intelligence server which includes one or more processors; generated by a sensor or more than one sensor; and, to a device amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of [Claim 1] receives sensor data amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
[Claim 8] receiving […] sensor data amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
[Claim 20] receiving […] sensor data amounts to mere data gathering since it does not add meaningful limitations to the receiving action performed, see MPEP 2106.05(g))
Each of the above additional element(s) therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and also add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as
[Claim 1] a sensor or set of sensors; generated by the sensor or sensors; and, to a device, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
an artificial intelligence server including a processor or several processors and an artificial intelligence engine, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank., MPEP 2106.05(f).
[Claim 8] generated by a sensor or sensors; and, to a device, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
by the artificial intelligence server which includes one or more processors, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank., MPEP 2106.05(f).
[Claim 20] generated by a sensor or more than one sensor; and, to a device, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
by the artificial intelligence server which includes one or more processors, e.g., requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank., MPEP 2106.05(f).
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as
[Claim 1] receives sensor data, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
[Claim 8] receiving […] sensor data, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
[Claim 20] receiving […] sensor data, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-7 and 9-19 reciting particular aspects of analyzing data to provide a nutritional recommendation for optimal personalized milk fortification such as
[Claim 2] wherein the sensor or sensors are spectrometers, spectrographs, or other suitable sensors;
[Claim 3] wherein the optimized nutrition recommendation is specific to the particular infant and the particular infant has a birthweight less than 5 pounds, 8 ounces;
[Claim 4] wherein the clinical data may include disease risk score information about the particular infant;
[Claim 5] wherein the clinical data include a risk score of the particular infant to contract a disease;
[Claim 6] wherein the optimized nutritional recommendation reflects the one or more disease risk scores;
[Claim 7] wherein the one or more disease risk scores include clinical data information about the pre-birth period, at-birth period, or post-birth period of the particular infant;
[Claim 9] receiving, by the artificial intelligence server that includes one or more processors, an indication that the optimized nutritional recommendation for milk fortification has been manually adjusted;
[Claim 10] wherein based on the manual adjustment, the artificial intelligence server that includes one or more processors, updates the optimized nutritional recommendation for milk fortification for subsequent recommendations for the particular infant;
[Claim 11] receiving, by the artificial intelligence server that includes one or more processors, feeding feedback;
[Claim 12] wherein the feeding feedback indicates an amount of milk effectively consumed by the particular infant;
[Claim 13] wherein the feeding feedback is correlated by the artificial intelligence server with growth of the infant over a period of time;
[Claim 14] wherein the artificial intelligence server suggests the presence of an ailment or disease in the particular infant based on the feeding feedback;
[Claim 15] wherein the artificial intelligence server uses both the clinical data specific to the particular infant and feeding feedback to determine a risk score for the likelihood of developing diseases or conditions which may be affected by nutrition, including but not limited to growth faltering, bronchopulmonary dysplasia, necrotizing enterocolitis, and sepsis, for a particular infant;
[Claim 16] wherein the optimized nutrition recommendation for milk fortification provided to a device is generated using machine learning;
[Claim 17] wherein the artificial intelligence server that includes one or more processors further updates the optimized nutrition recommendation for milk fortification based on outcomes of other infants and based on at least one shared clinical data point between the other infants and the particular infant;
[Claim 18] wherein the artificial intelligence server that includes one or more processors transmits the optimized nutritional recommendation for milk fortification to the device for graphical or textual display on the device;
and,
[Claim 19] wherein the processor provides a timestamp for feeding feedback which is correlated, by the artificial intelligence server which includes one or more processors, with infant growth, and wherein the correlation is provided graphically or textually to a device;
these italicized portions covers performance of the limitations in the mind but for recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans).
Step 2A Prong 2
Dependent claims 2, 9-11, and 13-19 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 2 ((wherein the sensor or sensors are spectrometers, spectrographs, or other suitable sensors), claim 9 (by the artificial intelligence server that includes one or more processors), claim 10 (the artificial intelligence server that includes one or more processors), claim 11 (by the artificial intelligence server that includes one or more processors), claim 13 (by the artificial intelligence server), claim 14 (the artificial intelligence server), claim 15 (the artificial intelligence server), claim 16 (to a device is generated using machine learning), claim 17 (the artificial intelligence server that includes one or more processors), claim 18 (the artificial intelligence server that includes one or more processors; and, to the device for graphical or textual display on the device), and claim 19 (the processor […] by the artificial intelligence server which includes one or more processors […] graphically or textually to a device) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f); add insignificant extra-solution activity to the abstract idea such as claim 9 (receiving […] an indication that the optimized nutritional recommendation for milk fortification has been manually adjusted), and, claim 11 (receiving […] feeding feedback) amounts to mere data gathering since it does not add meaningful limitations to the receiving performed, see MPEP 2106.05(g))). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B
Dependent claims 2, 9-11, and 13-19 recites additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f). Also, see [0023] which provides examples of computer devices, [0029] which provides examples of memory devices, and [0029] disclosing examples of processing devices. Dependent claims 9 and 11 amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i). There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Therefore, in consideration of all the facts, the present invention is not a patent-eligible invention under USC 101.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied 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) 1-8 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Orbach at al. (US20200393376A1) in view of Veerawali at al. (IN202011029891A) and further in view of Radmacher et al. (Fortification of human milk for preterm infants).
Regarding claim 1, Orbach discloses a sensor or set of sensors which analyze a sample of human milk ([0221] “According to some embodiments of the invention, the system may include at least one device made up of: A designated area for the nutritional/immunological stick, light/color sensors, a Bluetooth component, a battery pack, a memory chip and optionally an LCD screen, a USB port and a temperature sensor.” [0222] “According to some embodiments, the system may include one or more options or alternatives for the sampling element to be inserted into the—device of the system and consequentially enabling the analysis of the breast milk.”)
analyzes the sensor data to identify concentrations of macronutrients and or micronutrients in the sample of human milk ([0222] “According to some embodiments, the system may include one or more options or alternatives for the sampling element to be inserted into the—device of the system and consequentially enabling the analysis of the breast milk.” [0215] “According to some embodiments, the system may include specific sampling elements for different purposes, for example, a nutritional sampling element including, for example, one or more components adapted to identify amounts and/or concentrations of specific nutritional factors in the breast milk.”)
compares the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with nutritional guidelines for a particular infant ([0547] “According to some embodiments, a user of the sampling element will compare the results indicated upon the sampling element to the legend's colors, and may verify a correspondence with the expected protein concentration.” [0564] “Reference is made to FIG. 30, which illustrates an exemplary legend of desired concentration of Vitamin B1 in the breastmilk of a mother feeding an infant in correlation to the age of the infant.”)
compares the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with feeding protocols obtained from historical clinical data about one or more infants with similar clinical profiles to the particular infant ([0032] “According to some embodiments, there is provided herein a system for determining the nutritional needs of an infant, comprising a collection and/or analysis device, also referred to herein as a “sampling element”, to collect a sample of a maternal milk from a mother of said infant and to analyze the milk to measure at least one parameter; and a result indicator to provide a result, for example, a result which corresponds to a specific infant nutritional or immunological needs, e.g., indicating a specific formula to be administered to the specific infant.” [0046] “According to some embodiments, the calculation results may be compared to various other results, also referred to herein as the “previous results”. The previous results may include the user's results from past analysis, to the average results of other users in the data bank, and to results described in the relevant literature (also referred to herein as “gold standard”).”)
and provides to a device a nutritional recommendation for optimal personalized milk fortification based on the comparison of the macronutrients and or micronutrients in the sample of human milk with chosen nutritional guidelines ([0304] “According to some embodiments, device 802 may provide a recommendation of the specific brand and/or stage of the baby formula which best suits the actual needs of the baby, for example, device 802 may analyze the breastmilk of a mother breastfeeding a 5 month old baby and determine that the baby actually needs a stage 2 formula and not a stage 1 formula, based on the actual composition of the breast milk analyzed.”)
and with feeding protocols obtained from historical clinical data about one or more infants with similar clinical profiles for the particular infant, and optionally disease risk scores ([0174] “According to some embodiments, the calculation results may be compared to various other results, also referred to herein as the “previous results”. The previous results may include the user's results from past analysis, to the average results of other users in the data bank, and to results described in the relevant literature (also referred to herein as “gold standard”).”)
Orbach does not explicitly disclose however Veerawali teaches and an artificial intelligence server including a processor or several processors and an artificial intelligence engine ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
which: receives sensor data generated by the sensor or sensors ([0018] “In one implementation, Fog nodes, transfer the parameters received from the sensors to the cloud server for examination.”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches identifies one or more disease risk scores based on clinical data specific to the particular infant ([pg. 31] “Studied again at 30 months of age, these infants with increased volumes of human milk received during their neonatal hospitalization, continued to have higher Bayley Mental Developmental Index (MDI) scores.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use one or more disease risk scores as in Radmacher in order to determine the appropriate amount of nutrition an infant will need (See Radmacher, Introduction).
Regarding claim 2, Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches herein the sensor or sensors are spectrometers, spectrographs, or other suitable sensors ([pg. 31] “For example, using mid-infrared spectrophotometry we looked at the macronutrient content of DHM received from a regional milk bank and found a protein concentration of 1.0 g of protein per 100 mL of milk and energy content of ~15 calories per ounce.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to utilize sensors as in Radmacher to determine the amount of nutrition in milk (See Radmacher, Introduction).
Regarding claim 3, Orbach discloses wherein the optimized nutrition recommendation is specific to the particular infant ([0025] “accordingly adapting the proper formula for the infant based on his specific needs”)
Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches and the particular infant has a birthweight less than 5 pounds, 8 ounces ([pg. 30] “extremely low birth weight (ELBW, <1000 g) infant”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to target infants with birthweight less than 5 pounds, 8 ounces as in Radmacher to provide optimal nutrition and reduced complications (See Radmacher, Introduction).
Regarding claim 4, Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches wherein the clinical data may include disease risk score information about the particular infant ([pg. 30] “Poor growth during the neonatal hospitalization was associated with increased risk of cerebral palsy, MDI and Physical Developmental Index (PDI) scores <70, as well as increased risk of blindness and deafness at 18e22 months follow-up.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use disease risk score information as in Radmacher in order to determine the appropriate amount of nutrition an infant will need (See Radmacher, Introduction).
Regarding claim 5, Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches wherein the clinical data include a risk score of the particular infant to contract a disease ([pg. 30] “Poor growth during the neonatal hospitalization was associated with increased risk of cerebral palsy, MDI and Physical Developmental Index (PDI) scores <70”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use disease risk score as in Radmacher in order to determine the appropriate amount of nutrition an infant will need (See Radmacher, Introduction).
Regarding claim 6, Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches wherein the optimized nutritional recommendation reflects the one or more disease risk scores ([pg. 30] “Studied again at 30 months of age, these infants with increased volumes of human milk received during their neonatal hospitalization, continued to have higher Bayley Mental Developmental Index (MDI) scores and higher Bayley behavior score percentiles for emotional regulation, and fewer re-hospitalizations between discharge and 30 months.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use disease risk score as in Radmacher in order to determine the appropriate amount of nutrition an infant will need to reduce risk of disease (See Radmacher, Introduction).
Regarding claim 7, Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches wherein the one or more disease risk scores include clinical data information about the pre-birth period, at-birth period, or post-birth period of the particular infant ([pg. 30] “Development Neonatal Research Network, including nutritional data on 773 ELBW infants […] Studied again at 30 months of age, these infants with increased volumes of human milk received during their neonatal hospitalization, continued to have higher Bayley Mental Developmental Index (MDI) scores and higher Bayley behavior score percentiles for emotional regulation, and fewer re-hospitalizations between discharge and 30 months.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use disease risk score along with clinical information as in Radmacher in order to determine the appropriate amount of nutrition an infant will need to reduce risk of disease (See Radmacher, Introduction).
Regarding claim 8, Orbach discloses analyzing, […], the sensor data to identify concentrations of macronutrients and or micronutrients, contamination, and or freshness of a sample of human milk ([0222] “According to some embodiments, the system may include one or more options or alternatives for the sampling element to be inserted into the—device of the system and consequentially enabling the analysis of the breast milk.” [0215] “According to some embodiments, the system may include specific sampling elements for different purposes, for example, a nutritional sampling element including, for example, one or more components adapted to identify amounts and/or concentrations of specific nutritional factors in the breast milk.”)
comparing, […], the concentrations of macronutrients and or micronutrients in the sample of human milk from the analyzed sensor data with chosen nutritional guidelines;
([0547] “According to some embodiments, a user of the sampling element will compare the results indicated upon the sampling element to the legend's colors, and may verify a correspondence with the expected protein concentration.” [0564] “Reference is made to FIG. 30, which illustrates an exemplary legend of desired concentration of Vitamin B1 in the breastmilk of a mother feeding an infant in correlation to the age of the infant.”)
comparing, […], the concentrations of macronutrients and or micronutrients in the sample of human milk with feeding protocols obtained from historical clinical data about one or more infants from the analyzed sensor data with similar clinical profiles to the particular infant ([0032] “According to some embodiments, there is provided herein a system for determining the nutritional needs of an infant, comprising a collection and/or analysis device, also referred to herein as a “sampling element”, to collect a sample of a maternal milk from a mother of said infant and to analyze the milk to measure at least one parameter; and a result indicator to provide a result, for example, a result which corresponds to a specific infant nutritional or immunological needs, e.g., indicating a specific formula to be administered to the specific infant.” [0046] “According to some embodiments, the calculation results may be compared to various other results, also referred to herein as the “previous results”. The previous results may include the user's results from past analysis, to the average results of other users in the data bank, and to results described in the relevant literature (also referred to herein as “gold standard”).”)
and providing to a device, […], an optimized personalized nutritional recommendation for milk fortification, and optionally disease risk scores ([0304] “According to some embodiments, device 802 may provide a recommendation of the specific brand and/or stage of the baby formula which best suits the actual needs of the baby, for example, device 802 may analyze the breastmilk of a mother breastfeeding a 5 month old baby and determine that the baby actually needs a stage 2 formula and not a stage 1 formula, based on the actual composition of the breast milk analyzed.”)
Orbach does not explicitly disclose however Veerawali teaches receiving, […], sensor data generated by a sensor or sensors ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.” [0018] “In one implementation, Fog nodes, transfer the parameters received from the sensors to the cloud server for examination.”)
[…] by the artificial intelligence server which includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches identifying, […], one or more risk scores for a particular infant based on the likelihood of developing diseases affected by nutrition,
([pg. 31] “Studied again at 30 months of age, these infants with increased volumes of human milk received during their neonatal hospitalization, continued to have higher Bayley Mental Developmental Index (MDI) scores.”)
including but not limited to growth faltering, bronchopulmonary dysplasia, necrotizing enterocolitis, and sepsis ([pg. 30] “necrotizing enterocolitis (NEC), bronchopulmonary dysplasia (BPD)” [pg. 31] “infant may be experiencing growth faltering” [pg. 33] “Powder fortifiers are no longer recommended due to the risk of bacterial contamination and subsequent sepsis in the preterm infant [42,43].”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to use one or more disease risk scores as in Radmacher in order to determine likelihood of a specific disease in an infant (See Radmacher, Introduction).
Regarding claim 16, Orbach does not explicitly disclose however Veerawali teaches wherein the optimized nutrition recommendation for milk fortification provided to a device is generated using machine learning ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN), to detect the frequency and quality of feasible feed intake prediction as per the nutritional aspects of the child using the ANN technique.” [0020] “In one implementation, the results generated are displayed to the care giver’s Android-based smartphones is connected to the Smart Little’s feeder through Bluetooth.”))
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 17, Orbach discloses wherein […] further updates the optimized nutrition recommendation for milk fortification based on outcomes of other infants and based on at least one shared clinical data point between the other infants and the particular infant ([0367] “According to some embodiments, these notifications may include nutritional and/or dietary recommendations aimed to improve breast milk composition as a function of the nutritional test results. According to some embodiments, the system may allow a comparison between the present test values and previous tests stored in the device and/or application, allowing a continuous follow-up and recommendations for specific formulas for partially breastfed infants.” [0392] “Protein concentration and estimated the carbohydrates quantity in breast milk were determined and compared between 6 groups according to the age of the infant.”)
Orbach does not explicitly disclose however Veerawali teaches […] the artificial intelligence server that includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 18, Orbach discloses wherein […] transmits the optimized nutritional recommendation for milk fortification to the device for graphical or textual display on the device ([0038] “wherein the device may also provide a recommendation of a specific nutritional formula for the infant based on the at least one parameter, and/or provide a mother with nutritional recommendations to consume more or less of a specific nutrient.” [0614] “According to some embodiments, block 3412 details that the results may be displayed on dedicated application, e.g., a smartphone application, as well as suggestions for dietary changes or immunological concerns.”)
Orbach does not explicitly disclose however Veerawali teaches […] the artificial intelligence server that includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 19, Orbach discloses wherein the processor provides a timestamp for feeding feedback which is correlated, […], with infant growth ([0341] “According to some embodiments, the application may include the following features: User Friendly […] Growth monitoring section where the mother can input data and keep track of her infant's development.” [0351] “The mother's breastmilk's nutritional and immunological information is saved over time in the device's application, and can be used to monitor the child's growth and development.”)
and wherein the correlation is provided graphically or textually to a device ([0349] “The application will save this information for her convenience and will also be able to show her in graph form her infant's growth through.”)
Orbach does not explicitly disclose however Veerawali teaches […] by the artificial intelligence server which includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 20, Orbach discloses analyzing, […], the sensor or sensors data to identify concentrations of macronutrients and or micronutrients in a sample of human milk ([0222] “According to some embodiments, the system may include one or more options or alternatives for the sampling element to be inserted into the—device of the system and consequentially enabling the analysis of the breast milk.” [0215] “According to some embodiments, the system may include specific sampling elements for different purposes, for example, a nutritional sampling element including, for example, one or more components adapted to identify amounts and/or concentrations of specific nutritional factors in the breast milk.”)
comparing, […], the constituent elements of macronutrients and micronutrients in the sample of human milk from the analyzed sensor data with the chosen nutritional guidelines ([0547] “According to some embodiments, a user of the sampling element will compare the results indicated upon the sampling element to the legend's colors, and may verify a correspondence with the expected protein concentration.” [0564] “Reference is made to FIG. 30, which illustrates an exemplary legend of desired concentration of Vitamin B1 in the breastmilk of a mother feeding an infant in correlation to the age of the infant.”)
identifying, […] one or more disease risk scores to a particular infant based on one or more clinical data associated with information about the particular infant and his or her parents ([0602] “If the infant or mother is developing an infection, sIgA levels in the tested breastmilk would be elevated. For example, for an infant 2 months that is beginning to develop an infection sIgA levels should be over 1300 μg/ml.”)
and providing to a device, […], an optimized nutritional recommendation for milk fortification ([0304] “According to some embodiments, device 802 may provide a recommendation of the specific brand and/or stage of the baby formula which best suits the actual needs of the baby, for example, device 802 may analyze the breastmilk of a mother breastfeeding a 5 month old baby and determine that the baby actually needs a stage 2 formula and not a stage 1 formula, based on the actual composition of the breast milk analyzed.”)
Orbach does not explicitly disclose however Veerawali teaches receiving, by an artificial intelligence server which includes which includes one or more processors, sensor data generated by a sensor or more than one sensor ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.” [0018] “In one implementation, Fog nodes, transfer the parameters received from the sensors to the cloud server for examination.”)
[…] by the artificial intelligence server which includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Claims 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Orbach at al. (US20200393376A1) in view of Veerawali at al. (IN202011029891A) Radmacher et al. (Fortification of human milk for preterm infants) and further in view of Bhatnagar (US20170354771A1).
Regarding claim 9, Orbach does not explicitly disclose however Veerawali teaches […] by the artificial intelligence server which includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Orbach in view of Veerawali and Radmacher does not explicitly disclose however Bhatnagar teaches receiving, […], an indication that the optimized nutritional recommendation for milk fortification has been manually adjusted ([0086] “In some embodiments, milk in a refrigerated state may undergo a fortification process at the fortification state in unit state group 304.” [0150] “In various embodiments, system 100 maintains container identifier information (e.g., expiration date, container thawed status, container fortified status, milk type (e.g., colostrum, hind, fore, etc.), etc.).”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition, Veerawali’s techniques for feeding infants, Radmacher’s techniques for fortification of human milk, and Bhatnagar’s techniques for milk analytics. The motivation for the combination of Orbach, Veerawali, Radmacher, and Bhatnagar is to modify the system of Orbach, Veerawali, and Radmacher to receive an indication that milk has been fortified as in Bhatnagar to verify that the infant is indeed receiving the required nutrition (See Bhatnagar, Background).
Regarding claim 10, Orbach does not explicitly disclose however Veerawali teaches […] the artificial intelligence server that includes one or more processors […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Orbach in view of Veerawali and Radmacher does not explicitly disclose however Bhatnagar teaches wherein based on the manual adjustment, […], updates the optimized nutritional recommendation for milk fortification for subsequent recommendations for the particular infant ([0148] “In various embodiments, as shown in FIG. 7, workflow management module 162 processes user input for any workflow related workflow request 702 (e.g., combining a container, fortifying a container, splitting a container, feeding, etc.), then transmits the updates to management server 130.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition, Veerawali’s techniques for feeding infants, Radmacher’s techniques for fortification of human milk, and Bhatnagar’s techniques for milk analytics. The motivation for the combination of Orbach, Veerawali, Radmacher, and Bhatnagar is to modify the system of Orbach, Veerawali, and Radmacher to update the optimized nutritional recommendation as in Bhatnagar to ensure that the infant is indeed receiving the required nutrition (See Bhatnagar, Background).
Regarding claim 11, Orbach does not explicitly disclose however Veerawali teaches receiving, by the artificial intelligence server that includes one or more processors, feeding feedback ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.” [0035] “Based on these data values obtained, the acquired data is transmitted in real-time to a locally connected fog computation device in the form of Raspberry Pi.” [0041] “In some embodiments, freshness sensor, drinking reminder, and daily milk consumption can also be monitored.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 12, Orbach does not explicitly disclose however Veerawali teaches wherein the feeding feedback indicates an amount of milk effectively consumed by the particular infant ([0041] “In some embodiments, freshness sensor, drinking reminder, and daily milk consumption can also be monitored.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 13, Orbach discloses wherein the feeding feedback is correlated […] with growth of the infant over a period of time ([0351] “The mother's breastmilk's nutritional and immunological information is saved over time in the device's application, and can be used to monitor the child's growth and development.”)
Orbach does not explicitly disclose however Veerawali teaches […] by the artificial intelligence server […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Regarding claim 14, Orbach discloses wherein […] suggests the presence of an ailment or disease in the particular infant based on the feeding feedback ([0073] “According to some embodiments, there is provided herein a use of the sampling element for determining the amount or concentration of one or more immunological factor for alerting the development of an infectious disease in the infant or mother.”)
Orbach does not explicitly disclose however Veerawali teaches […] the artificial intelligence server […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition (See Veerawali, Background).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Orbach at al. (US20200393376A1) in view of Veerawali at al. (IN202011029891A), Radmacher et al. (Fortification of human milk for preterm infants), Bhatnagar (US20170354771A1), and further in view of O’Connor (US20100119617A1).
Regarding claim 15, Orbach does not explicitly disclose however Veerawali teaches […] the artificial intelligence server […] ([0015] “In one implementation, machine learning techniques like Bayesian models and Artificial Neural Networks (ANN)” [0032] “The real-time feed data is recorded and stored in the cloud server.”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition with Veerawali’s techniques for feeding infants. The motivation for the combination of Orbach and Veerawali is to modify the system of Orbach to incorporate artificial intelligence as in Veerawali in order to automate determinations typically performed by humans for milk nutrition for milk nutrition (See Veerawali, Background).
Orbach in view of Veerawali does not explicitly disclose however Radmacher teaches including but not limited to growth faltering, bronchopulmonary dysplasia, necrotizing enterocolitis, and sepsis for a particular infant ([pg. 30] “necrotizing enterocolitis (NEC), bronchopulmonary dysplasia (BPD)” [pg. 31] “infant may be experiencing growth faltering” [pg. 33] “Powder fortifiers are no longer recommended due to the risk of bacterial contamination and subsequent sepsis in the preterm infant [42,43].”)
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition and Veerawali’s techniques for feeding infants with Radmacher’s techniques for fortification of human milk. The motivation for the combination of Orbach, Veerawali, and Radmacher is to modify the system of Orbach and Veerawali to determine a specific disease in an infant as in Radmacher in order to determine the appropriate amount of nutrition to prevent that specific disease from occurring (See Radmacher, Introduction).
Orbach in view of Veerawali, Radmacher, and Bhatnagar does not explicitly disclose however O’Connor teaches wherein […] uses both the clinical data specific to the particular infant and feeding feedback to determine a risk score for the likelihood of developing diseases or conditions which may be affected by nutrition ([0070] “During the feeding intervention there was no difference between feeding groups in the volume of human milk ingested. As illustrated in FIG. 1, added nutrients resulted in early post-discharge gains in length, and head circumference measurements that were sustained until at least 6 months CA.” [0079] “The Bayley Scales of Infant Development-Second Edition (BSID-II, Psychological Corporation, San Antonio, Tex.) was administered by one of two qualified psychometrists, blinded to feeding assignment, at 18 mo CA (±10 days) (25).” [0080] “One infant in the control group had a PDI score indicative of significant delay”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to use Orbach’s techniques for monitoring breast milk composition, Veerawali’s techniques for feeding infants, Radmacher’s techniques for fortification of human milk, Bhatnagar’s techniques for milk analytics, and O’Connor’s techniques for nutrient fortification. The motivation for the combination of Orbach, Veerawali, Radmacher, Bhatnagar, and O’Connor is to modify the system of Orbach, Veerawali, Radmacher, and Bhatnagar to perform a determination of a risk score as in O’Connor to ascertain how multi-nutrient fortification is needed for the infant (See O’Connor, Background).
Response to Arguments
The arguments filed on 14 November 2025 have been considered, but are not fully persuasive.
Regarding the claim objection, applicant has corrected the status identifier of claim 8. Therefore, the claim objection has been withdrawn.
Regarding the USC 101 rejection, in response to the previous arguments by the office action, the applicant argues that the amended claims recite that the claimed comparisons are based on analyzed sensor data. Applicant asserts that even a human could identify characteristics of milk, there is no basis to suggest that any human is capable of detecting relative concentrations of macronutrients and or micronutrients in a sample. That the concentrations of macronutrients and or micronutrients in a sample is not detectable by humans. Applicant does not agree with the abstract idea characterization and asserts that claim recites patentable subject matter because it is not drawn to generic computer components and because it cannot be performed in the mind of a human being. Applicant asserts that claims 8 and 20 recite similar elements as claim 1 and are also drawn to patentable subject matter. In response to the office action’s argument that the specification [0003] to [0005] shows that the problem the applicant is solving is not a technological problem, applicant disagrees with the characterization of the specification and asserts that they have no admitted that that "previously the invention could be performed by doctors" anywhere at any time. Applicant states that standard care for "preemie" infants is based on "monitoring" and "highly sophisticated medical procedures." That this care is not based on a "sensor or set of sensors" which are used to identify "concentrations of macronutrients and or micronutrients" in a "human milk sample." Applicant denies any assertion by the USPTO.
Examiner disagrees with the applicant’s arguments. Examiner asserts the amendments still do not advance prosecution. The MPEP makes it clear that claims can recite an abstract idea even if they are claimed as being performed on a computer. The courts have also found claims requiring a generic computer or nominally reciting a generic computer may still recite abstract idea even though the claim limitations are not performed entirely by a human. The computers in the claims are not used in a specific, inventive way. The limitations identified as abstract in the present application are very outcome-based or result-focused and don’t give much technical detail that goes beyond what a human can do. For instance, in the aspect of the “sensor which analyzes a sample of human milk […] such as relative viscosity, smell, color, etc.” there is nothing in the claim limitation that tells the examiner how the sensor and artificial intelligence server are actually capable of analyzing a sample of milk beyond what a human can do.
The present artificial intelligence server claimed is a black box artificial intelligence with no clarity on the actual computer processing or how the computer is programmed to achieve the results in a non-abstract way different from how humans analyze/process data (see 2024 USPTO AI Guidance). Examiner still cannot be expected to believe that humans could not determine characteristics such as viscosity, smell, color, etc. Applicant asserted that there is no basis to suggest that any human being is capable of detecting relative "concentrations of macronutrients and or micronutrients" in a sample, but examiner points out that the applicant’s own claims and especially the specification provides the basis. A closer examination of the supporting disclosure for the comparison step in [0045] of the specification clearly discloses that a simple yes/no comparison to a nutritional guideline range is performed; generic computer devices are being used, but no specifics on how the generic devices are used. Thus, one of ordinary skill in the art would recognize that humans can analyze a food's weight and cross-reference it with food composition databases to allow for the determination of macronutrient and/or micronutrient content. Again, there is nothing in the claim limitation that tells the examiner how the sensor and artificial intelligence server are actually capable of performing a comparison of the concentrations of macronutrients and/or micronutrients in the sample of human milk beyond what a human can do. Examiner points out the applicant didn’t invent the concept of sensor detection or even the concept of performing analysis and comparison through sensors and an artificial intelligence server to now generically claim it and then call it non-generic. Examiner maintains that the claim does not go into detail on how the analyzing and comparison are actually performed beyond what a human can do and that is unique to the applicant’s invention. The computer components recited in the present claims are recited at a high level of generality. Applicant asserted that conventional care is not based on a sensor or set of sensors as performed in the present invention, but examiner counters by pointing out there is nothing inventive about adding on generic sensors to carry out the invention. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015) ("Just as Diehr could not save the claims in Alice, which were directed to ‘implement[ing] the abstract idea of intermediated settlement on a generic computer’, it cannot save OIP's claims directed to implementing the abstract idea of price optimization on a generic computer.") (citations omitted). Even if the claims nominally recite computer components that are rooted in technology, there is no recitation of how the computer components are specifically programmed to distinguish from generic computer processes.
Examiner further adds that the specification provides a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art. The MPEP provides that improvements to the functioning of a computer or to any other technology or technical field can signal eligibility, see MPEP 2106.05(a), and provides examples of improvements to computer functionality, MPEP 2106.05(a)(I), and improvements to any other technology of technical field, MPEP 2106.05(a)(I). “In computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead, invokes computers merely as a tool”. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). In Enfish, the court evaluated the patent eligibility of claims related to a self-referential database. Id. The court concluded the claims were not directed to an abstract idea, but rather an improvement to computer functionality. Id. It was the specification' s discussion of the prior art and how the invention improved the way the computer stores and retrieves data in memory in combination with the specific data structure recited in the claims that demonstrated eligibility. 822 F.3d at 1339, 118 USPQ2d at 1691. The claim was not simply the addition of general purpose computers added post-hoc to an abstract idea, but a specific implementation of a solution to a problem in the software arts. 822 F.3d at 1339, 118 USPQ2d at 1691. Unlike Enfish, the instant claimed invention appears to improve upon a judicial exception rather than a problem in the software arts. Rather than improving a computer's algorithm (i.e., solving a technically based problem), the claimed invention purports to solve the non-technological problem of the AAP & ESPGHAN guidelines failing to address nutritional needs that an infant may have due to the infant's clinical data, and the non-technological problem of analyzing a large quantity of data to accurately identifying risk factors such as growth faltering and other diseases which may be affected by nutrition ([0003] & [0005] of the specification) through generically applying computer components as a tool to customize a personalized fortification of human milk to feed to infants and to further identify risk scores for the onset of diseases based on the infant's clinical data. The applicant’s main/glaring issue is that specification does not show or describe a deficiency in the technology. Applicant’s own specification [0003]-[0005] literally admits that previously the invention could be performed by doctors, using established nutritional guidelines, in developing a feeding protocol for a preterm infant with a certain amount of monitoring, waiting, and checking. Applicant disagreed with the characterization of their own specification, but the disclosure of the specification is clear that conventional care did not require generic computers/sensors (as also stated by the applicant on pg. 4 of their arguments). Applicant is merely using computers to improve upon the judicial exception. That is, all the applicant is doing is applying computers/generic artificial intelligence for their intended benefit(s) to a new data environment and calling it an improvement (see Customedia Techs., LLC v. Dish Network Corp., Case No.18-2239 (Fed. Cir. Mar. 6, 2020). The examiner asserts the following facts are evident: 1) the invention does NOT involve a novel algorithm or data structure that significantly improves the computer's functionality, 2) the invention does NOT involve a new hardware component or configuration that works with the computer to achieve a specific technical benefit, and 3) the computer is NOT used in a completely new way demonstrating a significant technical advancement. Improvement to monitoring and managing medical and/or clinical events associated with the patient is not an improvement to computer technology. Thus, examiner does not see how the present claims improve the functioning of a computer or provide improvements to any other technology or technical field. The claimed invention appears similar to the example of improvements that are insufficient to show an improvement in computer-functionality such as arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019). See MPEP 2106.05(a)(I)(viii). The asserted benefits from the applicant’s invention seem to come as a result of the use of general-purpose computers. The broad claims are lacking concrete limitations to integrate the abstract idea into a practical application. Examiner points out that the claimed limitations have no indication in the specification that the operations recited invoke any inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (fed Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”). Most importantly, in DDR Holdings, the claims at issue specified how interactions with the Internet were manipulated to yield a desired result—a result that overrode the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113 USPQ2d at 1106. On the other hand, with the applicant’s claims, examiner points out there is nothing that tells the examiner how exactly the receiving, analyzing, comparing, identification, and providing limitations are actually performed through use of computer(s)/sensor(s). This further supports the examiner’s assertion that the present invention does not integrate the abstract idea into a practical application. To show an involvement of a computer assists in improving technology, the claims must recite details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology (MPEP 2106.05(a)(II)). In Finjan, Inc. v. Blue Coat Systems the courts found that the claims were “directed to a non-abstract improvement in computer functionality…” (MPEP 2106.04(d)). The present invention does not meet the condition set forth by the courts and thus does not integrate the judicial exception into a practical application. Claims 8 and 20 are also not drawn to patent eligible subject matter. The remaining dependent claims are still rejected under USC 101. The applicant has not demonstrated that their invention is inventive. Thus, the present invention is not patent-eligible under USC 101. Therefore, the USC 101 rejection has been maintained.
Regarding the USC 103 rejection, applicant argues on pages 11 to 14 that Orbach’s disclosure does not constitute as a comparison of concentrations of macronutrients and or micronutrients in the sample of human milk with nutritional guidelines for a particular infant. Applicant states that Orbach does not teach a comparison of any kind, and there’s simply no such comparison of analyzed milk nutritional concentrations to clinical data of another specific infant. Applicant also states that Veerwali and Radmacher do not remedy the deficiencies of Orbach. Applicant asserts that claims 1, 8, and 20 as well as their respective dependent claims are allowable. Applicant requests allowance.
Examiner disagrees with the applicant’s argument. Examiner points out that Orbach’s nutritional sampling element involves doing a comparison as further shown in [0547] which expands upon and clarifies the comparison performed. Examiner points out that both [0547] & [0564] have been cited, with [0215] excluded, to make it clear that the comparison performed by Orbach is a narrower interpretation of the generic language of performing a comparison of concentrations of macronutrients and or micronutrients in the sample of human milk with nutritional guidelines for a particular infant. Otherwise, one of ordinary skill in the art would clearly see that it doesn’t make sense why Orbach would go through the trouble of a sampling element and just stop there. Examiner still points out that the applicant’s generic claim doesn’t delineate the steps of how the comparison is specifically done to help distinguish from the prior art. Examiner also asserts that the additional cited portion from Orbach is a narrower interpretation for the comparison of the concentrations of macronutrients and/or micronutrients in the sample of human milk with feeding protocols obtained from historical clinical data about one or more infants with similar clinical profiles to the particular infant. Orbach does indeed have specific disclosure on such comparison of analyzed milk nutritional concentrations to data of another infant. It seems applicant has a different interpretation on what is included or excluded from the recited comparison, and so examiner requests the applicant clarify the record by with their interpretation. Claims 8, 20, and the remaining dependent claims are still rejected under USC 103. Therefore, the USC 103 rejection has been maintained and the claims are not allowable.
Prior Art Cited but Not Relied Upon
Sánchez, C., Franco, L., Regal, P., Lamas, A., Cepeda, A., & Fente, C. (2021). Breast milk: A source of functional compounds with potential application in nutrition and therapy. Nutrients, 13(3), 1026.
This reference is relevant because it investigates the contribution of these variants to disease susceptibility and phenotype in the Scottish early-onset IBD population.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINSTON FURTADO whose telephone number is (571)272-5349. The examiner can normally be reached Monday-Friday 8:00 AM to 4:00 PM EST.
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, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WINSTON R FURTADO/Examiner, Art Unit 3687
Prosecution Insights