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 .
Status of Claims
This is in response to applicant’s filing date of June 17, 2024. Claims 1-9 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority to Application KR10-2021-0181394, filed on December 17, 2021. The certified copy of the application as required by 37 CFR 1.55 has been received.
Priority Prior Filed Application
Applicant’s claim for the benefit of a prior-filed application, PCT/KR2022/019579 filed on 12/05/2022, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: communication unit; error correction model training unit; error correction unit; feed remaining amount calculation unit; feed management service provision unit; feed spoilage degree determination unit; step for: receiving a feed measurement distance value, training an error correction model, calculating an error-corrected feed measurement distance value, calculating a remaining amount of the feed, and providing a feed management service in claims 1-9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
On January 7, 2019, the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1-9 are directed toward non-statutory subject matter, as shown below:
STEP 1: Do claims 1-10 fall within one of the statutory categories? Yes. The claims are directed towards a system.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The system claims (1-9) are a mental process that can be practicably performed in the human mind and, therefore, an abstract idea. It merely consists of organizing components for an “feed management” such as keeping track of the amount of feed for a livestock in a feed bun or silo reservoir. This is equivalent to a person giving directions to a farmer that a feed in a bin needs to be replenish because is running low or because is spoiled or near complete spoilage . Notably, the claim does not positively recite any limitations regarding actual determination of the function perform by a feed management such as acquiring data and applying statistical methods that would provide an indication as to amount or spoilage given certain parameters. In fact, the claims in total appear to be a mere blue print for a farm with associated feed bins so as such a mere abstraction of the mind as being conceived by a planner such as architect.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1-9 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. While the claim does recite that the system/chamber for “feed management service”, there are no limitations in the body of the claim that recite determining and performing. Claims 1-9, as a whole, appear to be a recitation of a feed bin having possible data gathering devices to provide data to a farmer through a remote server that perform no function or interact with each other and appears to exist only in the abstract or in the mind of the designer.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1-10 do not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. A functional room with equipment for a medical procedure are fundamental, i.e. WURC, activities performed by a designer or architect, such as the feed bin in claims 1-9.
CONCLUSION
Thus, since claims 1-9 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1-9 are directed towards non-statutory subject matter.
Claim Rejections -- 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over ANN WONYEONG (KR-20190140530-A)(“Wonyeong”), machine translation of publication is attached hereto, and Ragot et al (US-20210262845-A1)(“ Ragot”).
As per claim 1, Wonyeong discloses a feed management service provision device that provides a feed management service in conjunction with a measurement device installed inside a feed bin of a farmhouse (Figures 1-2), comprising:
a communication unit configured to receive a feed measurement distance value indicating a distance from the measurement device to feed and environment data inside the feed bin from the measurement device (Wonyeong at Figure 2, sensing unit 200, and Para. [0107] discloses measuring the distance in the feed bin 100:” sensing unit 200 collects sensing data including measuring distance data using a laser sensor (S13).”);
;
;
a feed remaining amount calculation unit configured to calculate a remaining amount of the feed based on the Wonyeong at Para. [0042] discloses determining the remaining amount of feed remaining in the feed bin 100:” IoT sensing unit 200 in which the housing is designed as shown in FIG. 3 is used by including a laser sensor in the sensor unit 220 to measure the remaining feed amount in the feed bin 100”.); and
a feed management service provision unit configured to provide a feed management service to a user device based on the remaining amount of the feed (Wonyeong at Para. [0111][0112] discloses analyzing the data such as the remaining amount to provide to a server for replenishment and to provide a feed service to the farmer in terms of information, alarms, and the like:” the IoT smart gateway device 300 requests the feed order and feed price settlement together with the transmission of the sensing data to the feed management server 400 (S16).[0112] That is, the IoT smart gateway device 300 transmits the sensing data to the feed management server 400, but after collecting the sensing data by the feed management server 400, the mobile device 700 corresponding to the farm terminal for monitoring.”).
While recognizing that error in the distance measurements of the inside of the feed bin that is a critical variable in determining remaining amount, Wonyeong does not explicitly disclose a trained error correction model and an error correction unit for correcting received distance data. See Para. [0043] where Wonyeong discloses that inherent “error rate” from a distance measuring sensor can be mitigating through the use of averaging of the data points.
Wonyeong does not disclose, but Ragot discloses an error correction model training unit configured to train an error correction model using a plurality of pieces of training data including a plurality of feed measurement distance values for training collected from a plurality of feed bins (Ragot at Para. [0125] train a machine learning algorithm using sensed data from detectors 121 & 122:” ML algorithm must be trained with a significant amount of data (data tensors along with truth data). While training, the ML service will iteratively make predictive models.”), a plurality of pieces of environment data for training (Ragot at Para. [0022] includes measurement of environment data like temperature and the like:” measured temperature/humidity of the fluent material from a normalized or reference value.”), and a plurality of actual feed distance values corresponding to the plurality of feed measurement distance values for training (Ragot at Para. [0125] discloses training a predictive model to predict the amount of material passing through a space such as a pipe :” ML algorithm must be trained with a significant amount of data (data tensors along with truth data). While training, the ML service will iteratively make predictive models. At the end of the training step, the last issued predictive model will output values on its training data with a minimal error in comparison to the truth data.”)
Wonyeong does not disclose, but Ragot discloses an error correction unit configured to calculate an error-corrected feed measurement distance value from the feed measurement distance value and the environment data received by the communication unit using the trained error correction model (Ragot at Figures 1 & 7 and Para. [0108] discloses using a processor to correct the measurements using the learned model:” variety of implementations may exist, the goal is to correct sensor's defects when targeting the auger, the pipe and various heights of the fluent solid material. Optionally, the calibration procedure can be run several times for the same sensor with various types of feed placed into the calibration box at a time to increase the accuracy of distance range sensor data acquisition.”).
Ragot is considered to be analogous to the claimed invention because it is in the same field of systems which measures the amount of a material in a system like feed bin. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WONYEONG further in view of Ragot to allow for finding the quantity of a material with increased accuracy so that it can be replenish to feed the livestock in a farm setting. Motivation to do so would allow for reducing negative effects of service waiting time to replenish a feed bin in a farm by increasing the degree of accuracy of a feed management service, particularly in settings common for the commercial livestock industry as disclosed by Ragot at Para. [0005].
As per claim 2, WONYEONG and Ragot disclose a feed management service provision device of claim 1, wherein the error correction model training unit receives the plurality of feed measurement distance values for training and the plurality of pieces of environment data for training and trains the error correction model to output distance values that are close to the plurality of actual feed distance values (Ragot at Figure 16 and Para. [0125] the use of a machine learning mode to predict the flow and amount of material:” embodiment can determine the flow rate of the fluent solid materials using vibration sensor to measure the vibration in the pipe by applying ML methods (See FIG. 16). The ML algorithm must be trained with a significant amount of data (data tensors along with truth data). While training, the ML service will iteratively make predictive models. At the end of the training step, the last issued predictive model will output values on its training data with a minimal error in comparison to the truth data.”).
As per claim 3, WONYEONG and Ragot disclose a feed management service provision device of claim 2, wherein the error correction model is trained in a manner of outputting distance values by receiving the feed measurement distance values for training and the pieces of environment data for training, and updating weights based on results of comparing the output distance values with the actual feed distance values (Ragot at Figure 16 and Para. [0026] which discloses using relevant data such weight, distance, and the like to train the model to predict the amount of material:” Once training is completed, the model will then accurately predict the flow rate based on new input data. In order to be accurate, the predictive model must be trained with relevant data. To be relevant, the data must be correlated to the truth data to which it is linked. The constituent frequencies generated by the feeding system are chosen as the most relevant data to build an accurate predictive model. These vibrations can be acquired with audio pickups placed on the feeding pipe.”).
As per claim 4, WONYEONG and Ragot disclose a feed management service provision device of claim 1, wherein the environment data includes at least one of gas concentration, temperature, and humidity inside the feed bin (WONYEONG at Para. [0049] discloses sensing certain values relating to the feed in the bin:” the IoT sensing unit 200 may include a temperature sensor and a humidity sensor in addition to the laser sensor as the sensor unit 220 to measure the distance to the feed, the temperature humidity, and the like in the feed bin 100. The smart gateway device 300 may receive the distance data from the at least one IoT sensing unit 200 to calculate the remaining feed amount, and transmit sensing information such as the remaining feed amount, temperature, and humidity to the feed management server 400.”).
As per claim 5, WONYEONG and Ragot disclose a feed management service provision device of claim 4, further comprising a feed spoilage degree determination unit configured to calculate a daily feed change amount based on the remaining amount of the feed and determine a spoilage degree of the feed based on the gas concentration, the temperature, the humidity, and the daily feed change amount (WONYEONG at Para. [0112] discloses determining alteration of the feed which under the broadest interpretation is spoilage of the feed in the bin:” the IoT smart gateway device 300 may notify the farm terminal of the corresponding alarm when there is concern about alteration or contamination due to changes in temperature and humidity.”).
As per claim 6, WONYEONG and Ragot disclose a feed management service provision device of claim 5, wherein the feed spoilage degree determination unit calculates a high temperature and high humidity period in which the temperature and the humidity are measured to be higher than a set temperature and a set humidity (WONYEONG at para. [0112] monitors for changes in temperature and humidity to ascertain the contamination of the feed:” there is concern about alteration or contamination due to changes in temperature and humidity.”), respectively, determines the spoilage degree of the feed through a weighted sum of the gas concentration and the daily feed change amount when the high temperature and high humidity period exceeds a preset period, and determines the spoilage degree of the feed through a weighted sum of the high temperature and high humidity period, the gas concentration, and the daily feed change amount when the high temperature and high humidity period does not exceed the preset period (WONYEONG at Para. [0093] discloses using an average of the feeds at arrival and over time so it’s reasonable under the broadest of definition that an average can include a weighted average of the feed and respective temperatures:” referring to the average of the consumption of feed collected from other farms. To make this possible, farmhouse demand information is organized as follows. First of all, the arrival date and age of the livestock raising, the current feed amount and the availability of the feed amount can be predicted, and the scheduled order date and order amount can be calculated in advance. Not only is this information useful at the farm site, it also helps to ensure optimal feed stock so that fresh silos can always be supplied to the silos containing the feed in a timely manner from the point of view of the member companies supplying the feed.”) .
As per claim 7, WONYEONG and Ragot disclose a feed management service provision device of claim 6, wherein the feed management service provision unit provides feed replenishment notification information and a list of feed companies to the user device when the remaining amount of the feed is less than a first threshold or the spoilage degree of the feed exceeds a second threshold (WONYEONG at Para. [0101] discloses that a farmer using a mobile device 700 can receive updated information and on Para. [0114] that amount remaining is below a certain amount feed is ordered for the particular feed bin:” feed management server 400 predicts the shortage of the feed and determines the target of the online order when the remaining amount of the feed is less than or equal to the reference amount set in the corresponding feed bin 100. Feed management server 400 orders feed online.”).
As per claim 8, WONYEONG and Ragot disclose a feed management service provision device of claim 7, wherein the feed management service provision unit recommends at least one of a replenishment frequency of the feed, a replenishment amount of the feed, and a feed additive on the user device according to the spoilage degree of the feed (.WONYEONG at Para. [0098] using the frequency of feed deliveries to order feed for the particular farm:” system searches for a delivery schedule based on this information and proposes the most suitable dispatch target. The criteria are based on prioritized delivery schedules and regions, and records of frequent deliveries of the region or farm in the past.”)
As per claim 9, WONYEONG discloses a method of providing a feed management service that provides a feed management service in conjunction with a measurement device installed inside a feed bin of a farmhouse (Figure 14), comprising:
receiving a feed measurement distance value indicating a distance from the measurement device to feed and environment data inside the feed bin from the measurement device (Wonyeong at Figure 2, sensing unit 200, and Para. [0107] discloses measuring the distance in the feed bin 100:” sensing unit 200 collects sensing data including measuring distance data using a laser sensor (S13).”);
;
;
calculating a remaining amount of the feed based on the error-corrected feed measurement distance value, a volume of the feed bin, and a height of the feed bin (Wonyeong at Para. [0042] discloses determining the remaining amount of feed remaining in the feed bin 100:” IoT sensing unit 200 in which the housing is designed as shown in FIG. 3 is used by including a laser sensor in the sensor unit 220 to measure the remaining feed amount in the feed bin 100”.); and
providing a feed management service to a user device based on the remaining amount of the feed (Wonyeong at Para. [0111][0112] discloses analyzing the data such as the remaining amount to provide to a server for replenishment and to provide a feed service to the farmer in terms of information, alarms, and the like:” the IoT smart gateway device 300 requests the feed order and feed price settlement together with the transmission of the sensing data to the feed management server 400 (S16).[0112] That is, the IoT smart gateway device 300 transmits the sensing data to the feed management server 400, but after collecting the sensing data by the feed management server 400, the mobile device 700 corresponding to the farm terminal for monitoring.”).
While recognizing that error in the distance measurements of the inside of the feed bin that is a critical variable in determining remaining amount, Wonyeong does not explicitly disclose a trained error correction model and an error correction unit for correcting received distance data. See Para. [0043] where Wonyeong discloses that inherent “error rate” from a distance measuring sensor can be mitigating through the use of averaging of the data points.
Wonyeong does not disclose, but Ragot discloses training an error correction model using a plurality of pieces of training data including a plurality of feed measurement distance values for training collected from a plurality of feed bins (Ragot at Para. [0125] train a machine learning algorithm using sensed data from detectors 121 & 122:” ML algorithm must be trained with a significant amount of data (data tensors along with truth data). While training, the ML service will iteratively make predictive models.”), a plurality of pieces of environment data for training (Ragot at Para. [0022] includes measurement of environment data like temperature and the like:” measured temperature/humidity of the fluent material from a normalized or reference value.”), and a plurality of actual feed distance values corresponding to the plurality of feed measurement distance values for training (Ragot at Para. [0125] discloses training a predictive model to predict the amount of material passing through a space such as a pipe :” ML algorithm must be trained with a significant amount of data (data tensors along with truth data). While training, the ML service will iteratively make predictive models. At the end of the training step, the last issued predictive model will output values on its training data with a minimal error in comparison to the truth data.”)
Wonyeong does not disclose, but Ragot discloses calculating an error-corrected feed measurement distance value from the feed measurement distance value and the environment data received from the measurement device using the trained error correction model (Ragot at Figures 1 & 7 and Para. [0108] discloses using a processor to correct the measurements using the learned model:” variety of implementations may exist, the goal is to correct sensor's defects when targeting the auger, the pipe and various heights of the fluent solid material. Optionally, the calibration procedure can be run several times for the same sensor with various types of feed placed into the calibration box at a time to increase the accuracy of distance range sensor data acquisition.”).
Ragot is considered to be analogous to the claimed invention because it is in the same field of systems which measures the amount of a material in a system like feed bin. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified WONYEONG further in view of Ragot to allow for finding the quantity of a material with increased accuracy so that it can be replenish to feed the livestock in a farm setting. Motivation to do so would allow for reducing negative effects of service waiting time to replenish a feed bin in a farm by increasing the degree of accuracy of a feed management service, particularly in settings common for the commercial livestock industry as disclosed by Ragot at Para. [0005].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
GRAEBER MATTHIAS et al. (EP- 3859287-A1) INTELLIGENT SYSTEM CONTROL AND DOSING DEVICE;
SEO MAN HYOUNG (KR-20200065776-A) Livestock Breeding Apparatus;
Wynn; Ernest et al. (US- 20190387735-A1) Wireless Wildlife Observation Intelligence System;
Varikooty; Joseph Liu et al. (US- 20190018378-A1) SYSTEM AND METHOD FOR MONITORING STORAGE CONDITIONS IN PARTICULATE GOODS;
Leggett; Terry et al. (US- 20190014742-A1) AUTOMATED ANIMAL FEEDING SYSTEM AND METHOD OF USE;
Fromme, Guy A. et al. (US- 20040031335-A1) Bulk materials management apparatus and method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLIS B. RAMIREZ whose telephone number is (571)272-8920. The examiner can normally be reached 7:30 am to 5:00pm.
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, Ramon Mercado can be reached at 571-270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ELLIS B. RAMIREZ
Primary Examiner
Art Unit 3658
/ELLIS B. RAMIREZ/Examiner, Art Unit 3658
Prosecution Insights