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 .
Notice to Applicant
This communication is in response to application filed 8/14/2025. It is noted that application claims priority to JAPAN 2024-137156 filed 08/16/2024. Claims 1-3 are pending.
Information Disclosure Statement
Information disclosure statement dated 8/14/2025 has been acknowledged and considered.
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 1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-3 are drawn to system for aggregating and analyzing farm and livestock data, which is within the four statutory categories (i.e. machine).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system comprising a processor, wherein the processor is configured to: receive data collected from sensors installed in a livestock farm, preprocess the received sensor data and convert it into a format suitable for analysis, analyze, using artificial intelligence, abnormal behavior and body temperature changes based on the preprocessed data and calculate a risk score, generate and notify an alert when the risk score exceeds a certain threshold, propose preventive actions and countermeasures based on the risk score, and collect feedback data from a user and update an artificial intelligence model based on the feedback data.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to certain methods of organizing human activity – managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
The limitations of receiving data; preprocessing the data; analyzing the data; calculating a risk score; generating and notifying an alert; and proposing preventive actions and counteractions and update a model as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is directed to receiving information, receiving livestock information, analyzing it and proposing actions based on the analyzed data. Tracking information or filtering content has been found to be an abstract idea and a method of organizing human behavior. See MPEP 2106.04(a)(2)(II)(C). This is a method of organizing patient data thus falling into one category of abstract idea. That is other than reciting “processor” language, nothing in the claim element precludes the steps from describing concepts related to receiving and organizing patient data between people. If a claim limitation, under its broadest reasonable interpretation, covers concepts related to interpersonal and intrapersonal activities then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system comprising a processor, wherein the processor is configured to: receive data collected from sensors installed in a livestock farm, preprocess the received sensor data and convert it into a format suitable for analysis, analyze, using artificial intelligence, abnormal behavior and body temperature changes based on the preprocessed data and calculate a risk score, generate and notify an alert when the risk score exceeds a certain threshold, propose preventive actions and countermeasures based on the risk score, and collect feedback data from a user and update an artificial intelligence model based on the feedback data.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitations performing the functions by the at least one processor amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraph [0010] of the present Specification.
the recitation of using artificial intelligence recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– for example, the recitation of sensors installed in a livestock farm merely limits the abstract idea to the environment of livestock data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does 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 generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.”
Therefore, the additional elements do not add significantly more to the at least one abstract idea.
Dependent claim 2 merely specifies that the analysis is performed in real time. Performing the same abstract analysis in real time generally link the abstract idea to a particular technological environment or field of use. Dependent claim 3 merely expands the information analyzed by aggregating data collected from multiple livestock farms before performing the same predictive analysis which is also directed to certain methods of organizing human activity (collecting and filtering information).
Accordingly, the dependent claims, individually and as an ordered combination with the independent claims, fail to integrate the judicial exception into a practical application and fail to recite significantly more than the abstract idea itself.
Therefore, claims 1-3 are ineligible under 35 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Biffert (2022/0192150) in view of Schab (10,154,655).
As per claim 1, Biffert teaches a system comprising a processor (Biffert; para. 80, Fig. 3 the data collection, processing, storage, communications, and control components and elements of each tag 20 may include a processor and memory element 50), wherein the processor is configured to:
receive data collected from sensors installed in a livestock farm,
preprocess the received sensor data and convert it into a format suitable for analysis (Biffert; para. [0099] Each tag 20 is thus adapted and configured to communicate wirelessly and directly with the sensor ( s ) 32 of the livestock 12 to which the tag 20 , and with every other nearby tag 20 and every local sensor and transceiver 34 within Bluetooth signal range via their respective Bluetooth transceivers 84),
analyze, using artificial intelligence, abnormal behavior and body temperature changes based on the preprocessed data (Biffert; para. [0011] The remote computer system can also provide computing power and tools to generate , train , and update AI models and / or other detection - based algorithms for deter mining livestock activity , behavior and conditions using the masses of tag data and determinations) and
generate and notify an alert when the risk score exceeds a certain threshold (Biffert; para. [0043] Each tag 20 is also adapted and configured to generate and communicate alarms and/or alerts in response to certain detected activities and/or conditions, and to receive and respond to alarms and/or alerts), and collect feedback data from a user and update an artificial intelligence model based on the feedback data (Biffert; para. [0055] Such data and determinations can be used to create, develop, train, and subsequently update one or more machine learning or artificial intelligence (AI) models and/or one or more other detection algorithms for determining livestock activity, behavior, and conditions).
Biffert does not expressly teach a processor configured to: calculate a risk score, and propose preventive actions and countermeasures based on the risk score. However, this old and well-known in the art as evidenced by Schab. Schab; Col. 8, lines 31-35 teaches determining whether a composite value exceeds a predefined composite threshold value indicative of possible distress in the animal and notifying one or more remote caretakers of possible distress in the animal based on the composite value exceeding the predefined composite threshold value. This reads on calculating a risk score and proposing preventive actions and countermeasures based on the risk score. It would have been obvious to one of ordinary skill in the art to incorporate the distress evaluation and alert techniques of Schab into the livestock management system of Biffert because both references are directed toward automated monitoring of animal health using physiological and behavioral sensor information to provide earlier detection of abnormal animal conditions. Incorporating Schab's threshold-based evaluation into Biffert's livestock management platform merely represents the predictable use of one known animal monitoring technique to improve another for the purpose of providing earlier and more reliable health notifications.
As per claim 2, Biffert teaches the system according to claim 1, wherein the processor is further configured to analyze the preprocessed data in real-time and detect abnormal behavior (Biffert; para. [201] The sensors 32 can provide data continuously, periodically, or on demand by the tag 20; para. [0210]. The local sensors and transceivers 34 can communicate this data to the management system platform 140 to automatically determine and track if the livestock 12 is up to date on vaccinations, needed medicines, and/or medical procedures, and to generate alarms or alerts to a rancher, herd manager, etc. to take appropriate action)
As per claim 3, Biffert does not expressly teach the system according to claim 1, wherein the processor is further configured to aggregate data collected from a plurality of livestock farms and analyze and predict an overall situation.
However, this is an obvious variant of the Biffert teachings. Biffert para. [0216] teaches aggregating livestock data at a management platform and remote computer system, processing herd-related information, and generating livestock behavior and condition models. Although Biffert discusses herd-level aggregation, it would have been obvious to aggregate data from multiple livestock farms to increase the amount of available data and improve the accuracy and robustness of livestock condition analysis and prediction.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh (AU 2018220079 A1), the closest foreign prior art of record teaches a livestock health monitoring system that collects physiological and behavioral data from animals, preprocesses the collected data, and applies machine-learning techniques to classify animal health conditions and predict disease states. The reference further teaches using temperature, activity, and other sensor-derived parameters to identify abnormal animal behavior and provide health assessments.
Honeycutt (Honeycutt et al., “Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy.” University of Florida, Gainesville, Florida. 2020) the closest non-patent literature of record teaches interactive machine-learning systems in which user feedback is collected and used to correct model errors and update model parameters so that the model adapts over time. The reference demonstrates that feedback-based model refinement was well understood in the machine-learning art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm PST.
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LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 6/27/2026
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