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 .
Status of Claims
In the amendments filed on July 14, 2025, the following occurred: Claims 1, 4,5, and 10 were amended, and Claim 3 was cancelled.
Claims 1, 4-5, and 7-8, and 10 have been presented for examination and have been rejected in light of the aforementioned amendments. Such claims remain pending in the application.
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, 4-5, 7-8, and 10 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1, 4-5,7-8 and 10 are directed to a system and a method, which are statutory categories of invention. (Step 1: YES).
Step 2A – Judicial Exception Analysis, Prong One:
Independent Claims 1 and 10 recite perform an evaluation of a disease name of an animal, request an evaluation of the name of a disease, display a result that has been evaluated ... transmit(ting) a registration request for information about individual identification that includes moving image data that captures one animal at a time and individual information of each individual captured; and transmit a request for a disease name evaluation that includes moving image data in which [a] plurality of animals are captured ... in response to receipt of the registration request, extract a feature amount for the identification of an individual from moving image data that is included in the registration request, and register the feature amount and the individual information in association with each other; extract behavior data, which is time-series data of position information of each body part of a diagnosis target, from the moving image data; in response to receiving a disease name evaluation request, divides the moving image data that is included in the disease name evaluation request for each individual by identifying an individual that is a target of disease name evaluation in moving image data by using the feature amount, and evaluates a disease name based on the behavior data extracted from the divided moving image for each identified individual; and notify of a result of an evaluation ... display a notification configured to include the result of the evaluation.
The claims, as drafted, under their broadest reasonable interpretation, recite an abstract idea. Per MPEP 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers the management of personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims are directed to receiving and analyzing imaging data related to an animal in order to determine a disease state of said animal, wherein such determination is subsequently displayed to a user, influencing future action by the recipient. These steps are performed to facilitate routine disease monitoring of livestock and other animals, a process which is normally impractical for a livestock farmer to execute, even with the aid of other specialists (see: Specification, paragraph 2). Because the claims are directed to the handling of image data to yield a disease evaluation that ultimately informs human action, the claims are directed to certain methods of organizing human activity. Accordingly, the claims recite an abstract idea. (Step 2A, Prong One: YES).
Independent Claims 1 and 10 further recite generating a learning model for disease name evaluation based on moving image data by machine learning that uses moving image data for learning, a disease name, and individual information. The above limitation(s), in light of the 2024 Updated Guidance on Artificial Intelligence, is directed to mathematical concepts. Per paragraphs 63-64 of the instant Specification, the machine learning model is trained using algorithms for machine learning include the nearest-neighbor method, the naive Bayes method, decision tree, and support vector machine, while simultaneously contemplating the deployment of a neural network for feature weighting, all of which amount to algorithms that rely on mathematical relationships and calculations to refine the predictive capabilities of the resultant model. Accordingly, the training processes and generation of the model described above amount to mathematical concepts. Independent Claim 1 and 10 recites displays both the result of the evaluation and moving image data, and input of feedback information with respect to the result of the evaluation, and transmit feedback information with respect to the result of the evaluation that has been input by a user.
Dependent Claim 4 recites wherein the feedback information that has been obtained from the second information processing device corresponds to the moving image data.
Dependent Claim 5 recites wherein the feedback information is a disease name of a result of a diagnosis performed by a specialist that is input by a user.
Dependent Claim 7 recites wherein the individual information includes at least one of a classification, a breed, a sex, and an age of an individual.
Dependent Claim 8 recites wherein the request includes moving image data in which an animal is captured and individual information of the individual captured animal, and selects a model based on the individual information that is included in the evaluation request.
Each of the relevant features of the dependent claims only serve to further limit or specify the abstract features of independent Claim 1, and, hence, are nonetheless directed towards fundamentally the same abstract idea as the independent claim.
Step 2A – Judicial Exception Analysis, Prong Two:
The judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer (MPEP § 2106.05(f)).
The claims are abstract but for the recitation of the additional claim elements including “a system that includes an evaluation server that uses a learning model to,” (Claims 1 and 10) “a first information processing device configured to,” (Claims 1 and 10) “and a second information processing device configured to,” (Claims 1 and 10) “the first information processing device comprising: a memory storing instructions; and a processor executing the instructions causing the first information processing device to” (Claims 1 and 10) “to/by/from the evaluation server,” (Claims 1 and 10) “the evaluation server comprising: a memory storing instructions; and a processor executing the instructions causing the evaluation server to,” (Claims 1 and 10) “from the first information processing device,” (Claims 1 and 10) “by use of a learning model selected based on the individual information,” (Claims 1 and 10) “the second information processing device,” (Claims 1 and 10) “the second information processing device comprising: a memory storing instructions; and a processor executing the instructions causing the second information processing device to,” (Claims 1 and 10) “(in/on) notification page,” (Claims 1, 3, 5, and 10) “wherein the processor of the evaluation server further executes an instruction causing the evaluation server to generate a learning model for disease name evaluation based on moving image data by machine learning that uses moving image data for learning and a disease name,” and a UI for “and wherein the processor of second information processing device further executes an instruction to cause the evaluation server to, “wherein the processor of the evaluation server generates or updates a learning model by machine learning that uses moving image data in which disease name evaluation is performed, and the feedback information that has been obtained from the second information processing device that corresponds to the moving image data,” (Claim 4) “wherein the processor of the evaluation server,” (Claims 5 and 8) “by use of a selected learning mode “a learning model” (Claim 8).
The above-identified additional elements are recited in the limitations, described in the Specification, and represented in the Drawings in such a way that they can be reasonably construed to be generic computer or technological components implemented on or in conjunction with a general purpose computer. For example, the Specification and Drawings describe and depict, respectively, the processors that comprise the computer of the system as any one of a “central processing unit (CPU), micro processing unit (MPU),” (see: Specification, paragraph 96), and the processor or circuitry as optionally including “a graphics processing unit (GPU), or a field programmable gateway (FPGA). In addition, the processor or circuitry may also include a digital signal processor (DSP), data flow processor (DFP), or neural processing unit (NPU).” (see: Specification, paragraph 97). Similarly, the “evaluation server” is merely depicted in the Drawings as a commonplace computer server, devoid of any features that would distinguish it from standard hardware, and is described in the Specification only nominally and comprising what appear to be generic computer components (see: Specification, paragraph 25, where the evaluation server “includes a CPU 202, a ROM 203, a RAM 204, an HDD 205, an NIC 206, an input unit 207, a display unit 208, and a GPU 209, which are connected to each other by a system bus 201”). The generation of a predictive model, based upon image data, that is used to determine a disease state, is understood to constitute the training of a predictive model using standard data forms – e.g., image data, feedback information, breed, age, gender, etc. Because the training process is only stated in the claims and not detailed in any meaningful way, the generation of a model amounts to the high level recitation of technical elements, such that it amounts to more than mere instructions to apply the exception using a generic computer component. The remaining additional claim elements are also recited at a high level of generality and not sufficiently detailed in the disclosure to differentiate them from generic computer components or hardware. Therefore, the recitation of such technology amounts to mere instructions to implement the abstract idea using a general purpose technology (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”). Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014)).
Accordingly, the additional elements, alone or as an ordered combination, does not integrate the abstract idea into a practical application. (Step 2A, Prong Two: NO).
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer and/or the linking of the abstract idea to a technological environment. The elements identified to constitute the use of generic computer or technological components to implement the abstract idea are recited only as a tool for performing the steps of the abstract idea. These additional elements, therefore, only amount to mere instructions to perform the abstract idea using a general purpose computer and are, thus, insufficient to amount to significantly more than the abstract idea (see: MPEP § 2106.05(f) for additional guidance on the “mere instructions to apply an exception”). Furthermore, the arrangement of the additional claim elements has not been shown to fundamentally alter the performance of the elements individually. Imaging data is received by a processing device that inputs said data into a predictive model, which in turn, transmits the disease status determination to a second processing device for display. This organization of computer system components is not only widely prevalent in the art, but is not in itself inventive given its interchangeability with respect to the functioning of the claimed invention, and, thus, does not amount to significantly more than the abstract idea. This notion is even supported in the Specification, which, in paragraph 97, submits that “the computer may include a network of separate computers or separate processors to read out and execute the computer executable instructions,” indicating that the organization of the computerized components within the network is not integral to the invention as claimed. Therefore, alone or in combination, the additional claim elements do not amount to significantly more than the abstract idea. (Step 2B: NO).
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.
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.
Claim(s) 1, 7-8, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kanagasingham et al. (US 2016/0092721), hereinafter Kanagasingham, in view of Swisher et al. (US 2018/0300540), hereinafter Swisher, further in view of Tsubouchi (US 2014/0280273), hereinafter Tsubouchi, and further in view of Bates (US 2019/0221310), hereinafter Bates.
As per Claims 1 and 10, Kanagasingham teaches a system that includes an evaluation server that uses a learning model to perform an evaluation of a disease name of an animal (see: Kanagasingham, Abstract, paragraphs 1-2, 167, and 412, is met by a system for medical diagnosis of a biological subject, which can include nonhuman primates and other animals, that utilizes machine learning functionalities),
a first information processing device configured to request an evaluation of the name of a disease from the evaluation server (see: Kanagasingham, paragraphs 6 and 29, is met by the client device sending image data to the one or more electronic processing devices),
and a second information processing device configured to display a result that has been evaluated by the evaluation server (see: Kanagasingham, paragraphs 28, 216, and 246-248, fig. 2, is met by the display of a notification from the one or more electronic processing devices by either the original client device or a specialist client device), the system comprising:
the first information processing device comprising: a memory storing instructions (see: Kanagasingham, paragraph 227, is met by the client device(s) containing a built-in memory);
and a processor executing the instructions causing the first information processing device to (see: Kanagasingham, paragraphs 29 and 254-256, fig. 2, is met by the various computing devices having processors that execute stored instructions):
transmit a registration request for information about individual identification that includes image data that captures one animal at a time and individual information of each individual captured to the evaluation server (see: Kanagasingham, paragraphs 29-31, 136, and 167, fig. 1, is met by the client device transmitting image data of an individual subject to the one or more electronic processing system 210 in order to diagnose a disease of said subject);
and transmit a request for a disease name evaluation that includes image data in which a plurality of animals are captured to the evaluation server (see: Kanagasingham, paragraphs 183, 210, 240-244, is met by the diagnosis of a biological subject based image data received from the subject and numerous other subjects), the evaluation server comprising:
a memory storing instructions (see: Kanagasingham, paragraphs 249-250, figs. 2-3, is met by the processing system responsible for performing disease analysis containing a microprocessor and a memory); and a processor executing the instructions causing the evaluation server to (see: Kanagasingham, paragraphs 249-250, figs. 2-3, is met by the microprocessor executing the applications stored in the memory):
generate a learning model for disease name evaluation based on moving image data by machine learning that uses moving image data for learning, a disease name, and individual information (see: Kanagasingham, paragraphs 167, 183, 268, and 272, is met by the image processing and machine learning algorithms having specific execution styles for different disease conditions, a determination of which is partly influenced by imaging data, a repository of disease conditions from the EHR, and indicator values of specific diseases);
in response to receipt of the registration request, extract a feature amount for the identification of an individual from image data that is included in the registration request, and register the feature amount and the individual information in association with each other (see: Kanagasingham, paragraphs 261-263, is met by the processing system identifying subjects based on comparison between the received patient information and the image data);
and evaluates a disease name based on the behavior data extracted from the divided image data by use of a learning model selected based on the individual information (see: Kanagasingham, paragraphs 6, 167, 2689-269, 297, fig. 6, is met by the diagnosis of a biological subject based on the inputting of received image data into an applicable machine learning algorithm);
and notify the second information processing device of a result of an evaluation by the evaluation server (see: Kanagasingham, paragraphs 18, 24, and 28, is met by a notification of the evaluated condition being sent to the original or different client device), the second information processing device comprising:
a memory storing instructions (see: Kanagasingham, paragraph 227, is met by the client device(s) containing a built-in memory);
and a processor executing the instructions (see: Kanagasingham, paragraphs 29 and 254-256, fig. 2, is met by the various computing devices having processors that execute stored instructions) causing the second information processing device to:
display a notification page configured to include the result of the evaluation obtained from the evaluation server (see: Kanagasingham, paragraphs 28, 216, and 225, is met by the displaying of the notification, which includes the disease identification and evaluation of the biological subject, on the original client device or different client device).
While Kanagasingham discloses the use of image data in the evaluation of a disease state of a biological subject, Kanagasingham fails to specifically disclose that such image data includes moving image, as taught by Swisher (see: Swisher, paragraphs 34, 39, and 44-46, is met by the use of video stream of a patient in assessing a condition).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the type of data used in determining a disease state of Kanagasingham to include video stream data, as taught by Swisher, with the motivation of acquiring a continuous influx imaging data to better assess changing vitals of a patient (see: Swisher, paragraph 39).
Kanagasingham also fails to specifically teach the following limitation(s), which is/are taught by Swisher: in response to receiving a disease name evaluation request from the first information processing device, [the evaluation server] divides the moving image data that is included in the disease name evaluation request for each individual by identifying an individual that is a target of disease name evaluation in moving image data by using the feature amount (see: Swisher, paragraphs 4, 19-21, 24, 34, 39, and 54; is met by the parsing and/or segmentation of video and moving imaging data of plurality of patients to identify a single patient, along with an associated state or condition, based on patient attributes includes in the manually entered patient information from an image of multiple people).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the disease recognition process of Kanagasingham to entail the parsing or segmentation of video stream data to identify a single patient from a group of patients, along with an associated condition, based upon recognized patient attributes, as taught by Swisher, with the motivation of being able to continue monitoring patient conditions while they wait, among multiple patients, for allocation of medical resources (see: Swisher, paragraphs 3).
Kanagasingham and Swisher fail to specifically teach the following limitation(s), which is/are taught by Tsubouchi: extract behavior data, which is time-series data of position information of each body part of a diagnosis target, from the moving image data (see: Tsubouchi, paragraphs 28-29 and 35, is met by the collection of time-series position data of the observed animal, obtained from constant video and camera monitoring, in determining abnormal animal behavior).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the type of image data used to inform the disease evaluation process of Kanagasingham and Swisher to include time-series data of position information of a diagnosis target, as taught by Tsubouchi, with the motivation of better assessing the extent of abnormality of animal behavior by considering the timing of patterns of movement when under observation (see: Tsubouchi, paragraphs 31-35).
Kanagasingham further teaches wherein the processor of the evaluation server displays both the result of the evaluation and image data on the notification page (see: Kanagasingham, paragraphs 28, 216, and 225, is met by the displaying of the notification, which includes the disease identification indicator and evaluation of the biological subject, on the original client device or different client device).
While Kanagasingham discloses the use of image data in the evaluation of a disease state of a biological subject, Kanagasingham fails to specifically disclose that such image data includes moving image, as taught by Swisher (see: Swisher, paragraphs 34, 39, and 44-46, is met by the use of video stream of a patient in assessing a condition).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the type of data used in determining a disease state of Kanagasingham to include video stream data, as taught by Swisher, with the motivation of acquiring a continuous influx imaging data to better assess changing vitals of a patient (see: Swisher, paragraph 39).
Kanagasingham and Swisher fail to specifically the following limitation(s), which is/are taught by Bates: and a UI for the input of feedback information with respect to the result of the evaluation (see: Bates, paragraph 60, fig. 5, is met by the doctor being presented with a list of diagnoses from which to choose or edit and then choose), and wherein the processor of second information processing device further executes an instruction to cause the evaluation server to transmit feedback information with respect to the result of the evaluation that has been input by a user on the notification page to the evaluation server (see: Bates, paragraph 65, fig. 5, is met by the updating of the diagnosis module following the doctor’s feedback).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the system functionality of Kanagasingham and Tsubouchi to include a UI input for user feedback regarding the evaluation result and the subsequent transmission of the such feedback to the evaluation/diagnostic model, as taught by Bates, with the motivation of enhancing the robustness of the diagnostic process (see: Bates, paragraph 60).
As per Claim 4, Kanagasingham, Swisher, Bates, and Tsubouchi teach the limitations of Claim 1. Bates further teaches wherein the processor of the evaluation server generates or updates a learning model by machine learning in which disease name evaluation is performed, and the feedback information that has been obtained from the second information processing device (see: Bates, paragraphs 58 and 65, fig. 5, is met by the updating of the diagnosis module, which may comprise artificial intelligence (AI) integrated algorithm and/or machine learning algorithms following the doctor’s feedback).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to further modify the diagnostic system of Kanagasingham, Swisher, and Bates to additionally include input for user feedback regarding the evaluation result and the subsequent updating of the evaluation model upon receipt of such feedback, as further taught by Bates, with the motivation of enhancing the robustness of the diagnostic process (see: Bates, paragraph 60).
While Kanagasingham discloses the use of image data in the evaluation of a disease state of a biological subject, Kanagasingham fails to specifically disclose that such image data includes moving image, as taught by Swisher (see: Swisher, paragraphs 34, 39, and 44-46, is met by the use of video stream of a patient in assessing a condition).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the type of data used in determining a disease state of Kanagasingham, Swisher, and Bates to include video stream data, as further taught by Swisher, with the motivation of acquiring a continuous influx imaging data to better assess changing vitals of a patient (see: Swisher, paragraph 39).
As per Claim 5, Kanagasingham, Swisher, Tsubouchi, and Bates teach the limitations of Claim 3. Bates further teaches wherein the feedback information is a disease name of a result of a diagnosis performed by a specialist that is input by a user in the notification page (see: Bates, paragraphs 57-60, fig. 5, is met by the doctor selectively editing diagnosis results and inputting which presented and/or edited results to endorse).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to further modify the feedback information provided to the diagnostic system of Kanagasingham, Swisher, and Bates, to additionally include a disease name of a result of a diagnosis performed by a doctor, as taught by Bates, with the motivation of enhancing the robustness of the diagnostic process (see: Bates, paragraph 60).
As per Claim 7, Kanagasingham, Swisher, Tsubouchi, and Bates teach the limitations of Claim 1. Kanagasingham further teaches wherein the individual information includes at least one of a classification, a breed, a sex, and an age of an individual (see: Kanagasingham, paragraph 231, is met by the subject data that is used to determine which conditions should be screened for including age, gender, genetic attributes, and the like).
As per Claim 8, Kanagasingham, Swisher, and Tsubouchi teach the limitations of Claim 1. Kanagasingham further teaches wherein the request includes image data in which an animal is captured (see: Kanagasingham, paragraphs 6 and 29, is met by the transmission of image data of a biological subject to the one or more electronic processing devices) and individual information of the individual captured animal (see: Kanagasingham, paragraphs 39, 42, 231, is met by the analysis of subject data which can include age, gender, genetic information, and the like), and wherein the processor of the evaluation server selects a learning model based on the individual information that is included in the evaluation request (see: Kanagasingham, paragraphs 39, 42, 153, and 263, is met by the image processing and machine learning algorithms having specific execution styles for different disease conditions, a determination of which is partly influenced by subject data).
While Kanagasingham discloses the use of image data in the evaluation of a disease state of a biological subject, Kanagasingham fails to specifically disclose that such image data includes moving image, as taught by Swisher (see: Swisher, paragraphs 34, 39, and 44-46, is met by the use of video stream of a patient in assessing a condition).
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to modify the type of data used in determining a disease state of Kanagasingham to include video stream data, as taught by Swisher, with the motivation of acquiring a continuous influx imaging data to better assess changing vitals of a patient (see: Swisher, paragraph 39).
Response to Arguments
The arguments set forth in the Remarks filed on July 14, 2025 have been considered and will be addressed below in the order in which they appear.
Response to Arguments under 35 U.S.C. § 101:
In the Remarks, Applicant argues in substance that (1) the rejection(s) under 35 U.S.C. § 101 should be withdrawn because the claims do not recite an abstract idea, as the claims perform an evaluation of a disease in an animal, not a human.
Examiner respectfully disagrees; such arguments are unpersuasive. Per MPEP § 2106.04(a)(2), if a claim limitation, under its broadest reasonable interpretation, covers the management of personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claims are directed to the evaluation and displaying of a disease state of an animal based upon image data associated with said animal. These activities are used to “automate the health management of pets and livestock,” (see Specification, paragraph 2), specifically as it pertains to determining a disease status, which is often impractical for livestock farmers to manually perform, even with aid of a specialist (see Specification, paragraphs 2-4). The invention, however, still ultimately outputs a disease determination of an animal to a specialist or livestock farmer via the notification page, which is provided at the request of a human user. This is performed in the interest of influencing further action on the part of such caretakers, presumably including appropriate treatment plans, disease mitigation steps, and/or the like. Therefore, while the image data and disease evaluation concerns animals and livestock, the invention is used to be responsive to and subsequently direct the behavior of humans. Accordingly, because the claimed invention influences the personal behavior of humans, the limitations recite an abstract idea.
Applicant merely asserts that limitations in the independent claims do not properly fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, failing to offer any reasoning to substantiate this claim. Therefore, Examiner must simply reiterate the rationale stated in the prior paragraph for construing the present limitations as being directed to Certain Methods of Organizing Human Activity, and, thus, abstract subject matter.
In the Remarks, Applicant argues in substance that (2) the rejection(s) under 35 U.S.C. § 101 should be withdrawn because the additional claim elements provide a technical solution to a technical problem, thus integrating the abstract idea into a practical application. For the same rationale, Applicant asserts that the additional claim elements amount to significantly more than the abstract idea.
Examiner respectfully disagrees; such arguments are unpersuasive. Applicant submits that a technical improvement is achieved by execution of the claim limitations; however the purported improvement is described as an improvement in accuracy to behavior data extraction via automation of the machine learning model and detection of disease onset without having to go to the location of the subject. These “improvements” are not technical improvements analogous to those recognized by the court to reflect a practical application, as the “improvements” are accomplished simply by implementing the machine learning model and a camera. Cameras coupled to computer networks already allow one to capture data, transmit, and analyze data from remote geographical locations; this does not indicate an improvement to any of the technologies involved (the camera or computer) as they are merely being utilized to perform their expected functions in a paradigmatic manner. Similarly, the machine learning model is, of course, going to yield heightened accuracy in extracting behavioral data from an image data stream compared to manual data collection, given the task is now automated and less prone to human error. But such a benefit is intrinsic to any automated process; it’s one of the chief benefits to automation. Furthermore, the use of a machine learning model in its typical fashion notwithstanding, the improvement is not directed to the improvement of the model itself or the cameras/computer components themselves, but rather the performance of the data collection and analysis operations, which are constituents of the abstract idea. An improvement to the abstract idea cannot amount to a technical improvement, and, thereby, cannot represent a practical application.
In the Remarks, Applicant argues in substance that (3) the rejection(s) under 35 U.S.C. § 101 should be withdrawn because the claim involves significantly more than the abstract idea.
Examiner respectfully disagrees; such arguments are unpersuasive. For at least the same rationale as above, the additional claim elements do not amount to significantly more than the abstract idea. The ordered combination of the additional elements, when considered as a whole, does not amount to significantly more than the abstract idea. In essence, the claimed invention involves the receiving and analysis of moving image data in order to produce a disease evaluation, and the outputting of said evaluation to a user. That abstract process is facilitated by a paradigmatic scheme of technical components claimed at a high level of generality (e.g., a “learning model,” “a first and second processing device(s),” “a memory,” and “an evaluation server”) that perform their prescribed functions in a standard manner to obtain expected results (e.g., the processing devices receive, transmit, and display data; the memory and processor(s) of the evaluation server store and execute instructions, respectively, to evaluate the received data, etc.). The interaction between the individual elements is also typical insofar as the first processing device transmits data to the server, which subsequently applies a generic model to the data to yield a determinative result, and the server transmits that result to a second processing device for display to a user. This technical organization is widely prevalent in the art and accordingly cannot amount to significantly more than the abstract idea.
The rejection under 35 U.S.C. 101 is maintained.
Response to Arguments under 35 U.S.C. § 103:
In the Remarks, Applicant argues in substance that (4) the rejections under 35 U.S.C. § 103 should be withdrawn because Kanagasingham, Swisher, Tsubouchi, and Bates fail to specifically teach the amended features of the claims.
Examiner respectfully disagrees; such arguments are unpersuasive. First, Kanagasingham does affirmatively teach the generation of a machine learning model trained on imaging data, patient information, and disease information, as demonstrated in paragraphs 167, 183, 268, and 272, as well as others. The teachings of these paragraphs detail the use of machine learning algorithm capable of generating diagnostic predictions based upon disease indicator values associated with named diseases such as Alzheimer’s and glaucoma, patient electronic health records, and, of course, the image data from past predictions. Thus, Kanagasingham sufficiently teaches this amended limitation of the independent claims.
Second, Applicant argues that Kanagasingham and Swisher fail to disclose “extract[ing] behavior data, which is time-series data of position information of each body part of a diagnosis target, from the moving image data.” Applicant is correct that neither Kanagasingham nor Swisher are relied upon to teach this limitation; instead, Examiner cites Tsubouchi to disclose this limitation. Applicant provides no reasoning for the submissions that Tsubouchi insufficiently discloses the aforementioned limitation beyond a bare assertion that Tsubouchi and Bates “fail to cure the deficiencies of Kanagasingham and Swisher.” As such, Examiner is unable to respond to Applicant’s remarks other than a reiteration of the cited provisions in the Claim Rejection – § 103 section of this action.
The rejection of the claims under 35 U.S.C. § 103 is maintained.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jason Dunham whose telephone number is 571-272-8109. The examiner can normally be reached M-F, 7-4.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pinky Boveja, can be reached at 571-272-8105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights