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 .
Response to Amendment
Claims 1, 5-6, 8, and 12-13 have been amended. Claims 3, 7, and 10 have not been modified. Claims 2, 4, 9, and 11 have been cancelled. Claims 1, 3, 5-8, 10, and 12-13 are pending and are provided to be examined upon their merits.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 November 19, 2025 has been entered.
Response to Arguments
Applicant’s arguments filed November 19, 2025 have been fully considered but they are not persuasive. A response is provided below.
Applicant argues 35 U.S.C. §112 Rejections, pg. 7 of Remarks:
Examiner acknowledges Applicant amendment and withdraws the §112 rejection.
Applicant argues 35 U.S.C. §101 Rejections, pg. 8 of Remarks:
Applicant provides similar arguments to the Remarks provided on October 20, 2025. The previous reply is provided below for Applicant convenience. Any responses to new arguments are underlined for clarity.
Regarding Prong One Step 2A, Applicant argues that the claims are not abstract, drawing parallels to Example 39. Examiner respectfully disagrees. Unlike Example 39, which recites claim limitations that cannot be performed through human means ("applying one or more transformations to each digital facial image"), the instant application recites using machine learning models to perform activities that are typically performed by veterinary doctors. Claim limitations that Applicant highlights, such as "estimating health status of the domestic animal with reference to the information on the behavior of the domestic animal and a health criterion for the domestic animal", are activities that could otherwise be performed by a veterinary doctor for a patient. Thus, the claims are characterized as certain methods of organizing human activity as managing the personal behaviors of a veterinary doctor. Examiner further notes that using a machine learning model to perform a task does not preclude the claim from reciting an abstract idea of mental processes. Please see claim 2 of Example 47.
However, the Examiner notes that the claims are no longer characterized under an abstract idea of mental processes.
Regarding Prong Two Step 2A, Applicant argues that the claims provide a practical application by improving the accuracy of event occurrence information. Examiner respectfully disagrees, as improving the accuracy of estimating health and feedback on how to manage an animal is an improvement to the abstract idea of animal care and diagnosis. An improvement to the abstract idea does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). Furthermore, efficiency is not enough to amount to a practical application via an improvement to computer or technology under Step 2A Prong 2 (see MPEP § 2106.05(a)(I) examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)) (also see MPEP § 2106.05(f)(2) stating “"claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not provide an inventive concept (Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015)”), and, thus, the combination of the generic computer components do not provide a non-conventional and non-generic arrangement of known, conventional pieces; note this is applied to Step 2B as well as Step 2A Prong 2).
Applicant further argues that the claims effect a particular treatment or prophylaxis for a disease or medical condition by “providing the second breeding information to the user… wherein the second breeding information includes at least one of information on an action to be taken by the user for the domestic animal and information on a management method corresponding to the health status of the domestic animal”. Examiner notes that providing a recommended action to follow along with management methods is an abstract idea of managing the personal behaviors of the user. Additionally, there is no positively recited administration step of the treatment, but only the equivalent of recommending a treatment, the claim does not qualify as a prophylaxis step under Step 2A Prong 2.
In order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.
Examiner notes reviewing claim 2 of Example 49 may be helpful in overcoming the 101 rejection.
Regarding Step 2B, Applicant argues that the Office Action fails to meet the necessary burden outlined in the Berkheimer Memorandum. However, the consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements (processors, behavior recognition model, estimation model, device of the user, behavior sensor), alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer and using two generic algorithms. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Examiner submits that the claim citation of "wherein in the estimating step,...." is directed to the abstract idea and is not an additional element. The only additional element recited on pg. 14 of the Remarks is the machine learning-based estimation model, which is a generic model that is simply applied to perform the abstract idea of estimating the health status of an animal.
Applicant argues 35 U.S.C. §103 Rejections, pg. 9 of Remarks:
Applicant argues that Shaw fails to teach displaying the first breeding information to the user according to the health status, as human-based assessments are performed without considering the output of the assessment RL agent. Examiner respectfully disagrees.
The human-based assessments highlighted by Applicant are an optional/supplemental dataset that is not required by the Shaw ([0067], “In some implementations, the plan RL agent 300 may take into consideration human-based assessment(s) 110 in addition to, or alternatively than, the outputs from the assessment RL agent 200.”).
Instead the basis of health status, as required by claim 1 (“determining first breeding information on the domestic animal on the basis of the health status”), is derived from the subjective and objective biological data ([0065], “subjective biological data 104 may indicate lethargy, vomiting, as well as coughing for the given patient with each of these indications separately and may also be part of the input vector representing the current state of the world for the assessment RL agent 200. Finally, objective biological data 106 such as cytology samples and bloodwork may separately may also be part of the input vector representing the current state of the world for the assessment RL agent 200.” [0066], “Based on this observation, as well as other observations from objective and/or subjective biological data, the assessment RL agent 200 may take actions such as, for example, assessing the animal with hepatitis, hepatic (liver) lipidosis, hepatotoxicity, hepatic (liver) nodular hyperplasia, neoplasm—hepatic, hyperadrenocorticism, hepatic venous congestion, and/or diabetes mellitus.”).
These results from the Assessment RL Agent are output to a GUI of a user’s device, as shown in Figs. 6B-C ([0092], “FIGS. 6A-6C illustrate one such alternative series of GUIs 108. Specifically, as shown in FIG. 6A, the user of the GUI 108 selects patient Quasimodo 602. Upon selection of Quasimodo 602, the user is displayed the GUI 108 of FIG. 6B which indicates a strong abnormality for the presence of a moderate to large round soft tissue opacity with the lungs as indicated by selection 604, as well as a weaker abnormality for the presence of an enlarged liver as indicated by selection 606.”).
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Thus, Shaw teaches the amended claim limitation.
Applicant further argues that Madeley fails to teach wherein the machine learning model is “trained on the basis of the feedback, the second breeding information, and the information on the behavior of the domestic animal after the second breeding information is provided to the user”.
Applicant specifically notes that “The Office acknowledges that Shaw in view of Gelfand does not teach “wherein the estimating step, the health status is estimated using the machine learning-based estimation model that is trained on the basis of the feedback, the second breeding information, and information on a behavior of the domestic animal after the second breeding information is provided to the user””. However, that is not the language used in the prior Office Action.
Examiner notes that pgs. 18-19 explain wherein Shaw teaches wherein the machine learning-based estimation model is trained on the basis of the feedback and the second breeding information and pg. 23 explains wherein Madeley teaches wherein the machine learning-based estimation model is trained on the basis of information on the behavior of the domestic animal after the second breeding information is provided to the user. Thus, combining the functionalities of Shaw in view of Madeley would result in a machine learning model that is trained using all three types of information.
Although the basis of the rejection regarding this limitation is unchanged, Examiner notes that the organization of the rejection is modified for greater clarity.
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, 5-8, 10, and 12-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a method (claims 1, 3, 5-8, 10, and 12-13). Accordingly, claims 1, 3, 5-8, 10, and 12-13 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP §2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and /or c) mathematical concepts. MPEP §2106.04(a).
The Examiner has identified method claim 1 as the claims that represent the claimed invention for analysis, and is similar to system claim 8.
Claim 1:
A method performed in a system for monitoring an object, the system comprising one or more processors and the method comprising the steps of:
by the one or more processors, estimating information on a behavior of a domestic animal being estimated from sensor data measured by a behavior sensor for the domestic animal using a machine learning-based behavior recognition model,
by the one or more processors, estimating health status of the domestic animal with reference to the information on the behavior of the domestic animal and a health criterion for the domestic animal;
by the one or more processors, determining first breeding information on the domestic animal on the basis of the health status;
by the one or more processors, providing the first breeding information to a user by displaying the first breeding information on a screen of a device of the user according to the health status;
by the one or more processors, receiving the user's feedback on the first breeding information via an input means of the user's device;
by the one or more processors, determining second breeding information on the domestic animal on the basis of the feedback on the first breeding information;
by the one or more processors, providing the second breeding information to the user by displaying the second breeding information on the screen of the user's device; and
by the one or more processors, estimating information on a behavior of the domestic animal after the second breeding information is provided to the user from sensor data measured by the behavior sensor using the machine learning-based behavior recognition model,
wherein the first breeding information includes information on a diagnosis of the domestic animal and information on a breeding environment of the domestic animal,
wherein, the information on the diagnosis and the information on the breeding environment are estimated using a machine learning-based estimation model that is trained on the basis of a correlation between the information on the behavior of the domestic animal and the health criterion for the domestic animal, and the information on the diagnosis of the domestic animal and the information on the breeding environment of the domestic animal,
wherein the health status of the domestic animal is estimated as one of three or more states that are classified depending on a degree of healthiness of the domestic animal,
wherein the first breeding information is determined such that other information is at least partially included in the first breeding information according to the health status,
wherein the second breeding information includes at least one of information on an action to be taken by the user for the domestic animal and information on a management method corresponding to the health status of the domestic animal,
wherein, the health status is estimated using the machine learning- based estimation model that is trained on the basis of the feedback, the second breeding information, and the information on the behavior of the domestic animal after the second breeding information is provided to the user, and
wherein the information on the behavior of the domestic animal after the second breeding information is provided includes a change in the health status of the domestic animal that occurs when the user manages the domestic animal according to at least one of the action to be taken by the user for the domestic animal and the management method corresponding to the health status of the domestic animal.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity as managing personal behaviors. The claim elements are directed towards “estimating information on a behavior of a domestic animal”, “estimating health status of the domestic animal”, “determining second breeding information on the domestic animal” (which corresponds to “information on an action to be taken by the user for the domestic animal and information on a management method”), and “estimating information on a behavior of the domestic animal after the second breeding information is provided to the user”, which is diagnosing and monitoring the behavior and health of an animal. Diagnosing and monitoring the behavior and health of an animal condition falls under the abstract concept of managing personal behaviors, as diagnosing is a human activity that is regularly performed by veterinary specialists for their patients. It is important to note that the examples provided by the MPEP such as social activities, teaching, and following rules or instructions are provided as examples and not an exclusive listing. Furthermore, the user device facilitates an interaction between the user and the device by providing information to the user and obtaining input.
Accordingly, the claim recites at least one abstract idea.
Claim 8 is abstract for similar reasons.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the idea into a practical application. As noted at MPEP §2106.04 (ID)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional elements beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional elements” while the underlined portions continue to represent the at least one “abstract idea”):
Additional elements cited in the Claims:
One or more processors, a behavior sensor, a machine learning-based behavior recognition model, a machine learning-based estimation model, a screen of a device (1,8); a non-transitory computer readable medium (7)
Any computing devices (processors, user device) capable of performing the steps of the claim are taught at a high level of generality such that they are merely applied to perform the abstract idea. Pg. 11 of Applicant specification recites: “any type of digital equipment having a memory means and a microprocessor for computing capabilities, such as a smart phone, a tablet, a smart watch, a smart band, smart glasses, a desktop computer, a notebook computer, a workstation, a personal digital assistant (PDAs), a web pad, and a mobile phone, may be adopted as the device 400 according to the invention.” As any generic computing device is applied to perform the abstract idea, no specific, technical improvements are being made to the technology of computing devices.
Machine learning models (machine learning-based behavior recognition model, machine learning-based estimation model) are also taught at a high level of generality. Pg. 14, lines 11-16 of Applicant specification recites: “the behavior recognition model may be implemented using a variety of known machine learning algorithms. For example, it may be implemented using an artificial neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN), but is not limited thereto.” Pg. 19, lines 3-8 further recites: “Meanwhile, the machine learning-based estimation model may be implemented using a variety of known machine learning algorithms. For example, it may be implemented using an artificial neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN), but is not limited thereto.” No specific, technical improvements are being made to the field of machine learning as known machine learning algorithms are simply applied to perform the abstract idea.
The behavior sensor is also taught at a high level of generality. Pg. 10 of Applicant specification recites: “the sensor 300 according to one embodiment of the invention is digital equipment capable of connecting to and then communicating with the object monitoring system 200, and may include a known six-axis angular velocity/acceleration sensor… the sensor 300 according to one embodiment of the invention may include a different type of sensor other than the angular velocity and acceleration sensor, and may be inserted inside a body of a domestic animal (e.g., a calf).” No specific, technical improvements are being made to sensor technologies as any sensor that could be placed on or inserted inside an animal may be applied to perform the insignificant extra-solution activity of receiving data from.
The non-transitory computer readable medium is also taught at a high level of generality. Pg. 25, lines 3-11 of Applicant specification recites: “Examples of the computer-readable recording medium include the following: magnetic media such as hard disks, floppy disks and magnetic tapes; optical media such as compact disk-read only memory (CD-ROM) and digital versatile disks (DVDs); magneto-optical media such as floptical disks; and hardware devices such as read-only memory (ROM), random access memory (RAM) and flash memory, which are specially configured to store and execute program instructions.” No specific, technical improvements are being made to computer readable mediums as they are simply used to perform the insignificant extra-solution activity of storing data.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID)(A)(2).
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 3 and 10: These claims recite herein the information on the diagnosis includes at least one of at least one suspected symptom and at least one suspected causative agent of the domestic animal; which only serves to further limit the abstract idea of the diagnosis.
Claims 5 and 12: These claims recite wherein the information on the breeding environment includes at least one of bodily changes, physical changes, and managerial changes associated with the breeding environment of the domestic animal; which only serves to further limit the information.
Claims 6 and 13: These claims recite wherein the health criterion includes information on a past behavior of the domestic animal, and wherein the health status is estimated by comparing the information on the behavior with the information on the past behavior; which teaches an abstract idea of comparing information with historical data can be performed mentally. This claim further serves to limit the health criterion information.
Claim 7: This claim recites a non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of Claim 1; which teaches a computing product at a high level of generality with no specific, technical improvements to non-transitory computer-readable recording mediums.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
These claims 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, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
Amount to elements that have been recognized as activities in particular fields (such as Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), MPEP §2106.05(d)(II)(i);storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv)).
Dependent claims recite 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. Dependent claims recite additional subject matter which amount to limitations consistent additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 3, 5-7, 10, and 12-13, additional limitations which amount to elements that have been recognized as activities in particular fields, claims 3, 5-7, 10, and 12-13, e.g., performing repetitive calculations, Flook, MPEP §2106.05(d)(II)(ii); claims 3, 5-7, 10, and 12-13, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP §2106.05(d)(II)(iv). 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 conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1, 3, 5-8, 10, and 12-13 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 5-8, 10, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Shaw (US 20220044815) in view of Gelfand (US 20200175611) further in view of Madeley (WO 2021173571 A1) and Gritzman (US 20210045362).
Regarding claim 1, Shaw teaches a method performed in a system for monitoring an object, the system comprising one or more processors and the method ([0050], “The computing system may be used to execute instructions (e.g., program code or software) for causing the computing system to execute the computer code described herein.”) comprising the steps of:
by the one or more processors, estimating health status of the domestic animal with reference to the information on the behavior of the domestic animal and a health criterion for the domestic animal ([0061], “FIG. 1 may also utilize subjective biological data 104 for a given animal. This subjective biological data 104 may include subjective observations that are otherwise difficult to quantify or measure” [0062], “the owner of an animal may observe that the animal has been unusually lethargic;… and other types of subjective symptoms or behaviors” [0066], “Based on this observation, as well as other observations from objective and/or subjective biological data, the assessment RL agent 200 may take actions such as, for example, assessing the animal with hepatitis, hepatic (liver) lipidosis, hepatotoxicity, hepatic (liver) nodular hyperplasia, neoplasm—hepatic, hyperadrenocorticism, hepatic venous congestion, and/or diabetes mellitus.”). Examiner notes that a determination that an animal may have a medical issue denotes a meeting or failing a disease criteria.
by the one or more processors, determining first breeding information on the domestic animal on the basis of the health status ([0066], “the observation of an enlarged liver by the classification AI engine(s) 102 in combination with objective biological data 106 (e.g., high blood sugar levels) as well as subjective biological data 104 (e.g., frequent urination and increased thirst) may result in the assessment RL agent 200 assessing these observations as diabetes mellitus.”);
by the one or more processors, providing the first breeding information to a user by displaying the first breeding information on a screen of a device of the user according to the health status ([0092], “FIGS. 6A-6C illustrate one such alternative series of GUIs 108. Specifically, as shown in FIG. 6A, the user of the GUI 108 selects patient Quasimodo 602. Upon selection of Quasimodo 602, the user is displayed the GUI 108 of FIG. 6B which indicates a strong abnormality for the presence of a moderate to large round soft tissue opacity with the lungs as indicated by selection 604, as well as a weaker abnormality for the presence of an enlarged liver as indicated by selection 606.”);
by the one or more processors, receiving the user's feedback on the first breeding information via an input means of the user's device ([0072], “soliciting feedback from the treating physician or treating veterinarian as well as soliciting feedback from the patient or pet owner. For example, a survey can be provided that asks whether the assessment (i.e., action 208) provided by the assessment RL agent 200 was correct or not. These surveys may be provided via email and/or may be accessed through an application that has been downloaded to, for example, the user's smart phone or other computing device.”);
by the one or more processors, determining second breeding information on the domestic animal on the basis of the feedback on the first breeding information ([0067], “FIG. 3 discussed infra, the plan RL agent 300 may take action(s) based on the assessment(s) received from the assessment RL agent 200. In other words, the plan RL agent 300 may take diagnostic actions (e.g., recommendation of additional diagnostic tests to further confirm a given assessment received from the assessment RL agent 200) and/or treatment actions in order to assist with the assessment abnormality identified by the assessment RL agent 200. For example, the assessment of diabetes mellitus by the assessment RL agent 200 may result in the plan RL agent 300 recommendation of further bloodwork in order to confirm this assessment and/or treatment medication(s) such as anti-diabetic medication(s), weight loss recommendations, and dietary recommendations.”);
by the one or more processors, providing the second breeding information to the user by displaying the second breeding information on the screen of the user's device ([0067], “These treatments and/or recommendations may be then provided to, for example, a client computing device where they are displayed on a treatment/recommendation GUI 108.”); and
wherein the first breeding information includes information on a diagnosis of the domestic animal ([0066], “As but another non-limiting example, the observation of an enlarged liver by the classification AI engine(s) 102 in combination with objective biological data 106 (e.g., high blood sugar levels) as well as subjective biological data 104 (e.g., frequent urination and increased thirst) may result in the assessment RL agent 200 assessing these observations as diabetes mellitus.”),
wherein the first breeding information is determined such that other information is at least partially included in the first breeding information according to the health status ([0020], “the system includes: a classification artificial engine that takes as input radiographic images, and outputs classifications for various conditions of an animal; a subjective biological data storage device that stores subjective biological data for the animal; an objective biological data storage device that stores objective biological data for the animal; an assessment RL agent which takes as input the classifications for the various conditions of the animal from the classification artificial engine, the subjective biological data for the animal from the subjective biological data storage device, and the objective biological data for the animal from the objective biological data storage device, and outputs a determined set of assessments for the animal based on the inputs;”). Examiner interprets the output of the classification artificial engine and the objective biological data to encompass other information, as these types of information are not the subjective (behavior) information.
wherein the second breeding information includes at least one of information on an action to be taken by the user for the domestic animal and information on a management method corresponding to the health status of the domestic animal ([0067], “FIG. 3 discussed infra, the plan RL agent 300 may take action(s) based on the assessment(s) received from the assessment RL agent 200. In other words, the plan RL agent 300 may take diagnostic actions (e.g., recommendation of additional diagnostic tests to further confirm a given assessment received from the assessment RL agent 200) and/or treatment actions in order to assist with the assessment abnormality identified by the assessment RL agent 200. For example, the assessment of diabetes mellitus by the assessment RL agent 200 may result in the plan RL agent 300 recommendation of further bloodwork in order to confirm this assessment and/or treatment medication(s) such as anti-diabetic medication(s), weight loss recommendations, and dietary recommendations.”). Examiner interprets the weight loss and dietary recommendations to encompass the management method, as pg. 23 of Applicant specification notes that a management method may also refer to guidance.
Shaw does not teach the method comprising:
by the one or more processors, estimating information on a behavior of a domestic animal being estimated from sensor data measured by a behavior sensor for the domestic animal using a machine learning-based behavior recognition model,
by the one or more processors, estimating information on a behavior of the domestic animal after the second breeding information is provided to the user from sensor data measured by the behavior sensor using the machine learning-based behavior recognition model,
wherein the first breeding information includes information on a breeding environment of the domestic animal,
wherein, the information on the diagnosis and the information on the breeding environment are estimated using a machine learning-based estimation model that is trained on the basis of a correlation between the information on the behavior of the domestic animal and the health criterion for the domestic animal, and the information on the diagnosis of the domestic animal and the information on the breeding environment of the domestic animal,
wherein the health status of the domestic animal is estimated as one of three or more states that are classified depending on a degree of healthiness of the domestic animal,
wherein, the health status is estimated using the machine learning- based estimation model that is trained on the basis of the feedback, the second breeding information, and the information on the behavior of the domestic animal after the second breeding information is provided to the user, and
wherein the information on the behavior of the domestic animal after the second breeding information is provided includes a change in the health status of the domestic animal that occurs when the user manages the domestic animal according to at least one of the action to be taken by the user for the domestic animal and the management method corresponding to the health status of the domestic animal.
However, Gelfand does teach the method comprising:
by the one or more processors, estimating information on a behavior of a domestic animal being estimated from sensor data measured by a behavior sensor for the domestic animal using a machine learning-based behavior recognition model ([0029], “The platform may further be configured to predict behavioral or other activity data and may use a neural network engine and machine learning algorithms to determine whether certain activities or behaviors are occurring based upon the data input” [0078], “The method 200 begins at a first step S201 when the platform receives potential activity and/or behavioral data from any one or more communicatively connected sensors. For example, the system may receive barometric, pedometric, geographic, accelerometric, and other information from a user's smartphone when the user is walking a dog… The data comparison may be made in accordance with one or more algorithmic models for determining activity and/or behavior from one or more user-centric and/or animal-centric sensors.”);
wherein the first breeding information includes information on a breeding environment of the domestic animal ([0075], “results of algorithmic comparisons may identify that animals that live in a certain region are more likely to exhibit a particular medical symptom than similar animals that do not live within the region”). Under the broadest reasonable interpretation, a region in which animals live would also encompass the environment that the animal breeds in.
wherein, the information on the diagnosis and the information on the breeding environment are estimated using a machine learning-based estimation model that is trained on the basis of a correlation between the information on the behavior of the domestic animal and the health criterion for the domestic animal, and the information on the diagnosis of the domestic animal and the information on the breeding environment of the domestic animal ([0075], “the platform server 103 may employ a neutral network engine in association with the algorithm database 110 wherein combinations of aggregate data may be analyzed periodically for trends or correlations. For example, results of algorithmic comparisons may identify that animals that live in a certain region are more likely to exhibit a particular medical symptom than similar animals that do not live within the region” [0044], “the social media platform may determine current health conditions for a given animal based at least in part on analysis of the uploaded activity data and stored historical activity data and medical history.” [0095], “even historical physical tracking features as may be available using animal sensors or the like.” [0088], “the system generates alerts based upon the presence and probability of potential medical issues (S409). For example, one or more alerts may be generated upon the user interface for an animal profile with a relatively high risk of one or more of: hip dysplasia, cardiovascular disease, eczema, glaucoma, inguinal hernia, kidney disease, quadriplegia and amblyopia, urolithiasis, and so forth.” [0027], “associate detected behavior or likelihood of behavior with one or more animal-related products, and determine correlations between a type of detected behavior or likelihood of behavior and the one or more animal-related products. For example, the platform may identify key geolocational points where animals are determined to be likely to urinate and mark territory”). Examiner notes that health criterion refers to historical behavior/activity data, as indicated by dependent claims 6 and 13 and pg. 16, lines 24-26 of Applicant specification (“the health criterion may include information on a past behavior of the domestic animal”). Additionally, a determination that an animal may have a medical issue denotes a meeting or failing a disease criteria. Examiner further interprets a determination that the region in which an animal lives is connected to an animal’s health condition to encompass an estimation of the breeding environment by the machine learning.
Shaw in view of Gelfand are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw with Gelfand for the advantage of “upload[ing] animal-related information via… sensory input from one or more associated hardware devices” (Gelfand; [0029]) and receiving “geolocational, and other information from the user device” (Gelfand; [0076]).
Shaw in view of Gelfand does not teach the method comprising:
by the one or more processors, estimating information on a behavior of the domestic animal after the second breeding information is provided to the user from sensor data measured by the behavior sensor using the machine learning-based behavior recognition model,
wherein the health status of the domestic animal is estimated as one of three or more states that are classified depending on a degree of healthiness of the domestic animal,
wherein, the health status is estimated using the machine learning- based estimation model that is trained on the basis of the feedback, the second breeding information, and the information on the behavior of the domestic animal after the second breeding information is provided to the user, and
wherein the information on the behavior of the domestic animal after the second breeding information is provided includes a change in the health status of the domestic animal that occurs when the user manages the domestic animal according to at least one of the action to be taken by the user for the domestic animal and the management method corresponding to the health status of the domestic animal.
However, Shaw in view of Madeley does teach the method further comprising:
by the one or more processors, estimating information on a behavior of the domestic animal after the second breeding information is provided to the user from sensor data measured by the behavior sensor using the machine learning-based behavior recognition model (Madeley, [0006], “receiving sensor data from one or more sensors associated with the animal, the one or more sensors measuring movement-related parameters while the animal is engaged in two or more prescribed movement-related activities;… providing the calculated one or more movement-related metrics for each data slice to a movement base model that is trained to classify movement-related metrics into two or more movement scores, wherein the movement scores are associated with a movement-related condition of an animal, and wherein a first movement score indicates a movement-related condition in the animal and a second movement score indicates a healthy animal”),
wherein, the health status is estimated using the machine learning- based estimation model that is trained on the basis of
the feedback (Shaw, [0078], “The rewards 306 (e.g., as determined by the reward value function for the plan RL agent 300) may be characterized by soliciting feedback from the treating physician or treating veterinarian as well as soliciting feedback from the patient or pet owner similar to that described for the assessment RL agent 200. For example, a survey can be provided (e.g., via e-mail, in-app, paper, etc.) that asks whether the diagnostic and treatment recommendations (i.e., action 308) provided by the plan RL agent 300 was correct or not on a per-diagnostic or per-treatment recommendation basis. Similar to the assessment RL agent 200 discussion supra, it may be desirable to collect feedback via these surveys periodically over time to facilitate implementation and update of the plan RL agent's 300 longer-term reward functions and policy... By soliciting feedback over time, the plan RL agent 300 may be able to fine tune, for example, the actions 308 (e.g., treatments and/or additional diagnostics) that the plan RL agent 300 later takes.”),
the second breeding information (Shaw, [0077], “a given treatment plan may be initially given a highest level of confidence over other diagnostic and/or treatment plans given a set of assessments provided by the assessment RL agent 200 and accordingly would be prioritized for display on the treatment/recommendation GUI 108. This may be determined by diagnostic and/or treatment plans that have previously yielded the highest long-term rewards for a given set of assessments. However, another diagnostic test that has yet to be performed may be a strong indicator for the given treatment plan and may also be determined to be a quick and relatively inexpensive diagnostic test. In such an instance, the other diagnostic test may be subsequently prioritized over the given diagnostic and/or given treatment plan previously determined to strengthen (or weaken) the given diagnostic and/or given treatment plan. In some implementations, such a scheme may be implemented through a semi-supervised machine learning scheme, with the results being implemented in the reward value function for the plan RL agent 300 in order to further optimize the performance of the system 100 in future use.”) One of ordinary skill in the art would recognize that the treatment plans (second breeding information) would be used to further optimize/train the plan RL agent within the context of a semi-supervised machine learning scheme.
And the information on the behavior of the domestic animal after the second breeding information is provided to the user (Madeley, [0032], “Owners or veterinary doctors may use these movement scores to determine if the animal's health is improving when a particular treatment plan is implemented. For example, if the movement score is low, pain medication may be prescribed to the animal. Owners can then monitor the progress of their pet using the diagnostic system. An improvement in the movement score would indicate that the pain medication is working.” [0050], “receive sensor data from the sensing unit 104, process the received sensor data to identify one or more activities the subject may be engaged in, process the received sensor data to determine a movement score, maintain and train a movement base model, and communicate with the sensing unit 104 and the output unit 108.” [0118], “sensed activity data may be directly fed to the movement base model. In such cases, the movement base model is trained based on sensor activity data and not metrics data. For instance, in such cases, in method 500, step 508 may be omitted. Instead, the training data set at step 510 may include activity based sensor data and associated movement scores.”), and
wherein the information on the behavior of the domestic animal after the second breeding information is provided includes a change in the health status of the domestic animal that occurs when the user manages the domestic animal according to at least one of the action to be taken by the user for the domestic animal and the management method corresponding to the health status of the domestic animal (Madeley, [0032], “An improvement in the movement score would indicate that the pain medication is working. Alternatively, if the movement scores do not improve and/or improve for a period and then start declining again, it may be that the pain medication has not worked or has stopped working and the owner may consider changing the pet's pain medication and/or taking the pet back to see a veterinary doctor.”).
Shaw in view of Gelfand further in view of Madeley are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw in view of Gelfand with Madeley for the advantage of “train[ing] based on sensor activity data” (Madeley; [0118]).
Shaw in view of Gelfand further in view of Madeley does not teach wherein the health status of the domestic animal is estimated as one of three or more states that are classified depending on a degree of healthiness of the domestic animal.
However, Gritzman does teach wherein the health status of the domestic animal is estimated as one of three or more states that are classified depending on a degree of healthiness of the domestic animal ([0026], “The data can be used to calculate the overall health of the animal, which can be represented by an attribute to denote the degree of health, computed from the degree of deviation... The value of the attribute can have a range. For example, the pixel value may range from black (or green or another) for a healthy animal, to yellow (or another) for an animal displaying mild symptom of disease, to red (or another) for an unhealthy animal.”).
Shaw in view of Gelfand further in view of Madeley and Gritzman are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw in view of Gelfand further in view of Madeley with Gritzman for the advantage of “denot[ing] the degree of health” (Gritzman; [0026]).
Regarding claim 3, Shaw in view of Gelfand further in view of Madeley and Gritzman teaches the method of claim 1, as described above. Shaw further teaches wherein the information on the diagnosis includes at least one of at least one suspected symptom ([0062], “For non-human animals that are unable to verbalize their observations, the owners, or caregivers (e.g., veterinarians) for these animals must be relied upon to observe this subjective biological data. For example, the owner of an animal may observe that the animal has been unusually lethargic; has been vomiting; coughing; has unusually bad breath; is chewing or licking at its skin; has diarrhea or other issues with defecation; dragging its bottom; drooling; dizziness or difficulty maintaining balance; changes in the way the animal eats; reverse sneezing; seizures or trembling; excessive thirst; and other types of subjective symptoms or behaviors.”).
Shaw does not teach at least one suspected causative agent of the domestic animal.
However, Gelfand does teach at least one suspected causative agent of the domestic animal ([0075], “the platform server 103 may employ a neutral network engine in association with the algorithm database 110 wherein combinations of aggregate data may be analyzed periodically for trends or correlations. For example, results of algorithmic comparisons may identify that animals that live in a certain region are more likely to exhibit a particular medical symptom than similar animals that do not live within the region”). It would be obvious to one of ordinary skill in the art that a trend that shows that a region in which the animal lives may be a factor in a particular medical symptom may indicate that the region is a suspected causative agent.
Shaw in view of Gelfand are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw with Gelfand for the advantage of utilizing a system “wherein combinations of aggregate data may be analyzed periodically for trends or correlations” (Gelfand; [0075]).
Regarding claim 5, Shaw in view of Gelfand further in view of Madeley and Gritzman teaches the method of claim 1, as described above. Shaw in view of Gelfand does not teach wherein the information on the breeding environment includes at least one of bodily changes, physical changes, and managerial changes associated with the breeding environment of the domestic animal.
However, Madeley does teach wherein the information on the breeding environment includes at least one of bodily changes, physical changes, and managerial changes associated with the breeding environment of the domestic animal ([0008], “It would be desirable to facilitate aggregation of pet-related activity and behavior, which may further provide non-owner users beneficial information regarding animal health and activity information. For example, data mining of the behavioral and activity information may demonstrate heretofore unknown information about pet behaviors and activities that may improve the quality of pet-related products and services. Detecting pulling behaviors in large-breed dogs may suggest certain types of harnesses are more or less beneficial at training said large-breed dogs on proper leash behavior. Monitoring animal activity over time may reveal correlations between animal endurance and animal longevity... Aggregating traditionally disparate or non-collected pet information may desirably enable users to determine beneficial activities and change habits or decisions, providing favorable results with pets and animal care.”). Examiner interprets determining beneficial activities and changing habits or decisions to encompass managerial changes. Furthermore, it would be obvious to one of ordinary skill in the art that a decision to change a harness with a more beneficial harness to encompass a physical change.
Shaw in view of Gelfand further in view of Madeley are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw in view of Gelfand with Madeley for the advantage of tracking “events where user and/or animal information is added or changed” (Madeley; [0097]).
Regarding claim 6, Shaw in view of Gelfand further in view of Madeley and Gritzman teaches the method of claim 1, as described above. Shaw further teaches wherein the health criterion includes information on a past behavior of the domestic animal ([0063], “The system 100 may also include historical data which can be any input, whether subjective or objective, that has occurred in the past.”).
Shaw in view of Gelfand further in view of Madeley does not explicitly teach wherein the health status is estimated by comparing to the two types of information.
However, Gritzman does teach wherein the health status is estimated by comparing the information on the behavior with the information on the past behavior ([0036], “The system (e.g., one or more processors 102) may further recognize changes in health and behavior of an individual animal from its normal profile, and measure a degree of deviation... Via the augmented reality technique and device, the system may allow farmers or interested actor to visualize the historical movement of animals, for example, in the augmented reality glasses, and visualize the predicted next movement of the animals (e.g., moving toward water source). The system may also allow for visualizing the deviation from the established profile.” [0045], “The prediction can be determined, for example, by creating a machine learning model such as a regression model fitted with historical data, a neural network model trained based on historical data, and/or based on another prediction algorithm.”).
Shaw in view of Gelfand further in view of Madeley and Gritzman are considered analogous to the claimed invention because they are in the field of animal health determination. 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 Shaw in view of Gelfand further in view of Madeley with Gritzman for the advantage of “recogniz[ing] changes in health and behavior of an individual animal” (Gritzman; [0029]).
Regarding claim 7, Shaw in view of Gelfand further in view of Madeley and Gritzman teaches the method of claim 1, as described above. Gelfand further teaches a non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of Claim 1 ([0013], “a non-transitory computer-readable storage apparatus is disclosed. In one embodiment, the non-transitory computer-readable storage apparatus includes a plurality of instructions, that when executed by a processor apparatus, are configured to”).
Regarding claims 8, 10, 12, and 13, these claims are rejected for the same reasons as claims 1, 3, 5, and 6, respectively. Regarding claim 12, Examiner notes that Fig. 8 supports a disjunctive construction as the checklist separates each type of change. Thus, the claim does not require the prior art to teach support for all 3 types of information in one embodiment.
Conclusion
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/D.C./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684