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 .
DETAILED ACTION
Claims 1-12 have been examined.
Response to Arguments
Applicant's arguments filed April 01, 2026 have been fully considered but they are not persuasive. Followings are Applicant’s Arguments and Examiner’s response:
Applicant’s Arguments: While not conceding the appropriateness of the Examiner's rejection, but merely to advance prosecution of the instant application, amended claim 7, which incorporates the features of paragraphs 30 and 31 of the subject application, is presented below:
A herd behavior pattern analysis method using a herd behavior pattern
analysis apparatus of video-based herd objects, the method comprising:
receiving, through a data transmission/reception module, an input
video captured through at least one camera allocated to a space where
herd objects are accommodated;
performing video pre-processing to detect an edge image of a herd
object based on the input video; and
determining whether the herd object is normal by inputting the
edge image into a herd pattern analysis model to detect pattern
information of the herd object,
wherein the herd pattern analysis model is a machine-learned
model trained using learning data including the edge image of the herd
object, and outputs the pattern information of the herd object, and
wherein the pattern information of the herd object represents a visual
form of the herd object determined by a contour of the herd object or an
internal pattern of the contour based on the input video. (emphasis added)
With the amendment, Applicant respectfully asserts that claim 7 is more than adequate to overcome the 101 rejection for the following reasons: I. Step 2A, Prong 1: Amended claim 7 does not recite an abstract idea.
According to MPEP 2106.04(a)(2), "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRIInt'l, Inc. v.
Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019) (declining to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims"); CyberSource, 654 F.3d at 1376, 99 USPQ2d at 1699 (distinguishing Research Corp. Techs. v. Microsoft Corp., 627 F.3d 859, 97 USPQ2d 1274 (Fed. Cir. 2010), and SiRF Tech., Inc. v. Int'l Trade Comm'n, 601 F.3d 1319, 94 USPQ2d 1607 (Fed. Cir. 2010), as directed to inventions that "could not, as a practical matter, be performed entirely in a human's mind")."
Applicant respectfully submits that the features of "receiving, through a data
transmission/reception module, an input video captured through at least one camera allocated to a space where herd objects are accommodated; performing video pre-processing to detect an edge image of a herd object based on the input video; and determining whether the herd object is normal by inputting the edge image into a herd pattern analysis model to detect pattern information of the herd object, wherein the herd pattern analysis model is a machine-learned model trained using learning data including the edge image of the herd object, and outputs the pattern information of the herd object" in currently amended claim 7 are directed to a specific, multi-step technical process of digital image transformation.
That is, Applicant respectfully asserts that the claimed invention does not contain features that can practically be performed in the human mind since the human mind is not equipped to perform the claimed features. In fact, as identifying herd patterns for humans can be challenging to automate due to subjective human perception, the claimed invention is technically characterized by its ability to automatically recognize standardized patterns via a herd pattern analysis model based on video-preprocessing. In addition, a configuration has been added to
receive input video through a data transceiver module and process it within the herd pattern analysis model. Moreover, it is further specified that the herd pattern analysis model is based on machine learning. Applicant respectfully asserts that these features render the Examiner's "mental process" rationale moot.
That is, the features of this complex digital signal processing cannot be performed in the human mind since the claimed features require technical tools, and the human mind is not equipped to perform the very features. In other words, the video-preprocessing step and the pattern detection step using the machine-learned herd pattern analysis model in amended claim 7 cannot be reasonably categorized as mental processes/steps by humans. Human brains may desire for the specialized data processing, but is unable to obtain them via sheer mental processes/steps since the requirement of the special technical tools. Because the recited steps cannot practically be performed in the human mind, they do not fall within the "Mental Processes" grouping of abstract ideas.
Because the claim, taken as a whole, falls within a statutory category (Step 1: YES) and is not directed to a judicial exception (Step 2A: NO), Applicant respectfully requests the allowance of the claim.
II. Step 2A, Prong 2: Amended claim 7 does recite additional elements that integrate the
judicial exception into a practical application under the second prong of Step 2A because the claim comprises an additional feature or a combination of features which demonstrates an improvement to the conventional binary video processing technology,
as discussed in MPEP § 2106.05(a).
According to MPEP 2106.04(d), the Federal Circuit has distinguished between claims that are "directed to" a judicial exception (which require further analysis to determine their eligibility) and those that are not (which are therefore patent eligible), e.g., claims that improve the functioning of a computer or other technology or technological field. See Diamond v. Diehr, 450
U.S. 175, 209 USPQ 1 (1981); Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972). See, e.g., MPEP §2106.06(b) (summarizing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 118 USPQ2d 1684 (Fed. Cir. 2016), McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 120 USPQ2d 1091 (Fed. Cir. 2016), and other cases that were eligible as improvements to technology or computer functionality instead of being directed to abstract ideas). Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
If it is asserted that the invention improves upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.
Applicant respectfully asserts that at least paragraphs 3 and 4 of the subject application identify a technical problem faced by the conventional monitoring means, as presented in the following:
[0003] Previously, when an animal infectious disease was prevalent, abnormal behavior, posture, or changes in the appearance of individual animals were visually observed and confirmed to determine whether there was an abnormality.
However, there were many difficulties in individually confirming the objects for
the animal groups to be monitored.
[0004] In particular, these days, in the case of large-scale farms with a large number of herd objects, it is difficult to quickly respond to animal infectious diseases because too much effort and time are required to determine the status of
each object. (underlines added)
Furthermore, Applicant respectfully asserts that amended claim 7 is integrated into a
practical application of the remote, real-time, non-contact monitoring of livestock health, which
allows for early detecting of infectious diseases. This is a concrete utility that extends beyond the
merely gathering and organizing of information, as presented in paragraphs 11-13 of the subject
application as in the following:
[0011] In addition, when a simple imaging device is introduced into existing equipment, it is possible to confirm whether objects have infectious diseases,
other diseases, or changes in health and welfare through the herd behavior pattern
analysis apparatus of the present disclosure.
[0012] In particular, the present disclosure has an effect of being able to respond
very quickly to the spread of infectious diseases because it allows simultaneous
observation of various objects.
[0013] In addition, since the present disclosure corresponds to a non-face-to- face/non-contact testing method, it is much safer than the prior art, and infectious diseases may be detected remotely at an early stage through constant monitoring,
thereby providing a significant effect compared to the prior art. (underlines
added)
Because the claim comprises the combination of features which demonstrate an
improvement to the conventional technology, amended claim 7 integrates the judicial exception
into a practical application under the second prong of Step 2A.
Accordingly, for at least these reasons, Applicant respectfully asserts that independent
claim 7 and its dependent claims describe patent-eligible subject matter.
III. Step 2B: Amended claim 7 provides significantly more to the technical field.
As stated above, amended claim 7 of the present invention provides significantly more to the technical field since the claim recites a combination of features which improve upon the well- understood, routine, conventional livestock monitoring technology.
Accordingly, the amended claims do not merely use a generic computer to analyze information. Rather, they recite a specific technical process that improves pattern recognition technology itself. The claims integrate the pattern analysis into a practical application (Step 2A, Prong 2) and provide "significantly more" than an abstract idea (Step 2B) by reciting a specific technological improvement to overcome a recognized flaw in existing herd monitoring techniques.
Accordingly, for at least these reasons, Applicant respectfully submits that the 35 U.S.C. § 101 rejection has been successfully overcome. Because the Examiner has already indicated that the claims are otherwise allowable, Applicant respectfully requests that the pending claims be passed to issue.
Currently amended claim 1 is also patentable for at least the same reasons as independent claim 7, as claim 1 includes substantially the same features as claim 7.
Claims 2-6 and 8-12 are also patentable for the same reasons as their respective independent claim, as well as on their own merits.
Thus, it is respectfully requested that these rejections under 35 U.S.C. 101 be reconsidered and withdrawn.
Examiner respectfully disagrees.
Upon reconsideration of Applicant’s remarks and the amendments to independent claims 1 and 7, the rejection under 35 U.S.C. § 101 is maintained. Applicant’s arguments have been fully considered but are not persuasive for the reasons discussed below.
I. Step 2A, Prong 1 – The Claims Remain Directed to an Abstract Idea
Applicant contends that the amended claims are not directed to a mental process because the recited steps require technical tools (camera input, edge detection, and a machine-learned model) and therefore cannot practically be performed in the human mind.
This argument is not persuasive.
When evaluated under the broadest reasonable interpretation and as a whole, independent claims 1 and 7 remain directed to the abstract idea of:
receiving information (video data),
extracting features from the information (edge images),
analyzing the extracted features using a model (including a machine-learned model), and
making a determination (whether the herd object is normal).
These steps fall within the mental processes grouping of abstract ideas, as they collectively recite observation, evaluation, and judgment based on visual information.
The use of a “machine-learned model” does not remove the claims from this category because the model is recited only functionally (i.e., receiving edge images and outputting pattern information) without any claimed technical improvement to machine learning technology itself.
Similarly, “detecting an edge image” and “determining whether the herd object is normal” remain abstract data analysis steps that correspond to human cognitive processes when provided with visual information, even if implemented using a computer.
Accordingly, the claims are directed to an abstract idea.
II. Step 2A, Prong 2 – The Abstract Idea Is Not Integrated Into a Practical Application
Applicant argues that the claims provide a technical solution to livestock monitoring problems and improve conventional video processing and herd monitoring technology.
This argument is not persuasive because the improvement is not recited in the claims.
The claims do not recite a specific improvement to:
video processing technology,
edge detection algorithms,
machine learning architecture or training methods,
image feature extraction techniques,
or computer performance.
Instead, the claims recite generic functional operations:
receiving video via a data transmission/reception module,
performing video preprocessing,
detecting edge images,
inputting data into a machine-learned model,
and outputting a classification.
These limitations merely apply the abstract idea using generic computer components operating in their expected manner.
The recitation that the system is used for “herd objects in a space” merely limits the abstract idea to a particular environment and does not amount to a technological improvement.
The asserted benefits described in the specification (e.g., remote monitoring, early disease detection, reduced manpower) are results of applying generic computing techniques and do not constitute a claimed improvement to computer functionality or another technology.
Accordingly, the abstract idea is not integrated into a practical application.
III. Step 2B – The Claims Do Not Recite Significantly More Than the Judicial Exception
Applicant argues that the claims provide a specific technical process that improves pattern recognition technology and thus include “significantly more” than the abstract idea.
This argument is not persuasive.
The additional elements include:
a processor,
a memory,
a data transmission/reception module,
at least one camera,
video preprocessing operations,
and a machine-learned model.
These components, individually and in combination, are well-understood, routine, and conventional computer elements performing their ordinary functions of:
collecting data,
storing data,
processing data,
analyzing data, and
outputting results.
The “machine-learned model” is recited in purely functional terms without any technical details regarding:
model architecture,
training methodology improvements,
feature engineering innovations,
or computational enhancements.
As such, the claims amount to nothing more than applying generic machine learning and image processing techniques to a particular field (livestock monitoring).
This is insufficient to supply an inventive concept.
IV. Examiner’s Response to Applicant’s Step 2B Arguments (Significantly More)
Applicant asserts that amended claim 7 provides “significantly more to the technical field” because it allegedly recites a combination of features that improve conventional livestock monitoring technology, improves pattern recognition technology itself, and therefore constitutes a specific technological improvement overcoming flaws in prior systems.
These arguments are not persuasive.
1. “Provides significantly more to the technical field”
Applicant’s assertion is not commensurate with the scope of the claims. Independent claims 1 and 7 recite generic steps of:
receiving video data,
performing edge detection preprocessing,
applying a machine-learned model,
and determining whether a herd object is normal.
These limitations are implemented using generic computing components (processor, memory, camera, and data transmission/reception module) performing their routine functions. No claim limitation reflects a technical improvement to computer functionality or any other technology.
Accordingly, the claims do not provide “significantly more” than the abstract idea.
2. “Recites a specific technical process that improves pattern recognition technology itself”
Applicant’s argument is not persuasive because the claims do not recite any improvement to pattern recognition technology itself.
The claims do not specify:
any improvement to the machine-learning architecture,
any improvement to training methodology,
any improvement to feature extraction beyond generic “edge image detection,”
or any improvement to inference efficiency or accuracy mechanisms.
Instead, the claims merely invoke a machine-learned model as a functional black box that receives input and produces output.
The mere application of machine learning to a new type of data (herd video) does not constitute an improvement to the technology of machine learning itself.
3. “Improves conventional livestock monitoring technology / overcomes recognized flaws”
Applicant’s reliance on alleged improvements in livestock monitoring is not persuasive because such improvements are not reflected in the claim limitations.
The alleged benefits (e.g., improved monitoring efficiency, early disease detection, reduced manpower) are results of using a generic computer system in a particular environment and do not constitute a claimed technical solution.
The claims do not recite:
a new monitoring architecture,
a new sensing system,
a new video processing pipeline,
or any unconventional hardware or software configuration.
Limiting the abstract idea to “herd objects in a monitored space” merely confines its use to a particular field of use and does not transform the abstract idea into a patent-eligible application.
4. Conclusion under Step 2B
When considered individually and as an ordered combination, the additional claim elements amount to no more than:
generic data acquisition,
generic preprocessing,
generic machine learning analysis,
and generic output classification.
These elements are well-understood, routine, and conventional computer functions and do not provide an inventive concept.
Accordingly, Applicant’s Step 2B arguments do not demonstrate that the claims recite “significantly more” than the judicial exception.
The rejection under 35 U.S.C. § 101 is therefore maintained.
VI. Applicant’s Mental Process Argument Is Not Persuasive
Applicant asserts that the claimed invention cannot be performed in the human mind.
This argument is not persuasive because the proper inquiry is not whether the entire system can be performed mentally in real time, but whether the claimed steps recite abstract mental processes when considered at a high level of generality.
Here, the steps of:
observing video,
identifying edge features,
comparing patterns, and
determining normality
are all fundamentally evaluative and cognitive in nature.
The use of generic computer implementation does not convert these abstract analytical steps into a technological improvement.
VII. Conclusion
For the reasons above:
The claims remain directed to an abstract idea (Step 2A, Prong 1),
The abstract idea is not integrated into a practical application (Step 2A, Prong 2), and
The claims do not include an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter (Step 2B).
Accordingly, the rejection of claims 1–12 under 35 U.S.C. § 101 is maintained, and Applicant’s arguments are respectfully unpersuasive.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 7 recite a system (claim 1) and a method (claim 7) for analyzing herd behavior patterns of video-based herd objects.
Independent claim 1 recites receiving, through a data transmission/reception module, an input video captured through at least one camera allocated to a space where herd objects are accommodated; performing video pre-processing to detect an edge image of a herd object based on the input video; and determining whether the herd object is normal by inputting the edge image into a herd pattern analysis model to detect pattern information of the herd object, wherein the herd pattern analysis model is a machine-learned model trained using learning data including the edge image of the herd object and outputs the pattern information of the herd object, and wherein the pattern information represents a visual form of the herd object determined by a contour of the herd object or an internal pattern of the contour based on the input video.
Similarly, independent claim 7 recites receiving an input video, performing video pre-processing to detect an edge image of a herd object based on the input video, and determining whether the herd object is normal by inputting the edge image into a herd pattern analysis model to detect pattern information of the herd object, wherein the herd pattern analysis model is a machine-learned model trained using learning data including the edge image of the herd object and outputs the pattern information of the herd object, and wherein the pattern information represents a visual form of the herd object determined by a contour of the herd object or an internal pattern of the contour based on the input video.
The claims recite the abstract idea of receiving information, analyzing information, and classifying information based on detected patterns extracted from video data. In particular, the limitations of receiving an input video, detecting an edge image from the input video, inputting the edge image into a herd pattern analysis model, detecting pattern information representing a contour or internal pattern of a herd object, and determining whether the herd object is normal, collectively recite observation, evaluation, and classification of information. Under the broadest reasonable interpretation, these limitations amount to a mental process because they encompass collecting information, analyzing the information, and making a determination based on the analysis. The recited machine-learned model is used as a tool for performing the analysis and classification and does not, by itself, remove the claimed subject matter from the mental process grouping of abstract ideas.
This judicial exception is not integrated into a practical application.
The additional limitations include a processor, memory, data transmission/reception module, at least one camera, video pre-processing, and a machine-learned model trained using learning data including edge images. These additional elements do not integrate the abstract idea into a practical application because they are recited at a high level of generality and merely collect, process, and analyze information in order to determine whether a herd object is normal. The claims do not recite a specific improvement to computer functionality, machine-learning technology, image-processing technology, camera technology, or network technology. The claims do not specify a particular machine-learning architecture, a particular model-training technique, a specific edge-detection algorithm, a specific contour-extraction technique, or any technological mechanism by which computer performance or image-processing performance is improved. Instead, the claims use generic computing components and a broadly recited machine-learned model to analyze video information and produce a classification result. The recitation that the pattern information represents a visual form of the herd object determined by a contour of the herd object or an internal pattern of the contour merely describes the content of the information being analyzed and does not reflect a technological improvement. Likewise, receiving video from a camera allocated to a space where herd objects are accommodated merely limits the use of the abstract idea to a particular technological environment and constitutes insignificant extra-solution activity.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims recite additional elements including a processor, memory, data transmission/reception module, camera, video pre-processing operations, and a machine-learned model trained using learning data including edge images. When considered individually and in combination, these elements amount to no more than generic computer components performing conventional functions of receiving data, storing data, processing data, analyzing data, and outputting a result. The claims do not recite any unconventional arrangement of hardware components, any improvement to the operation of a computer, any improvement to machine-learning functionality, or any specific technological solution to a technological problem. The machine-learned model is invoked as a generic analytical tool and is claimed in terms of the result achieved rather than a specific technical implementation. Accordingly, the additional elements merely implement the abstract idea using generic computer technology and do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter.
Dependent claims 2-6 and 8-12 are rejected because they depend from claims 1 and 7 and do not recite additional limitations that amount to significantly more than the abstract idea for the reasons discussed above.
Dependent claims 2-6, 8-12, they are rejected based on the dependency of claims 1 and 7.
Allowable Subject Matter
Claims 1-12 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HOI C LAU/Primary Examiner, Art Unit 2689
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