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 Arguments
Rejection Under 35 U.S.C. § 101
Applicant’s arguments with respect to the rejection of claims 1-3, 6, 7, 9-11, 14, 15, 17, 18, 21, and 22 pages 10-15, under 35 U.S.C. § 101, filed on 08/07/2026, have been fully considered and are persuasive. The rejection is withdraw for the reasons below.
Examiner agreed that the claims are eligible because, as an ordered combination, show how solve an identify technological problem veterinary ML systems inadequately process animal-patient records paragraphs 0007, 0049, and 0060.
Rejection Under 35 U.S.C. § 103
Applicant’s arguments with respect to the rejection of claims 1-3, 6, 7, 9-11, 14, 15, 17, 18, 21, and 22 pages 16-21, under 35 U.S.C. § 103, filed on 08/07/2026, have been fully considered and are smoot. The rejection is withdraw and new obvious rejection was submitted.
Examiner consider applicant argument smoot, since previous rejection Lascelle and Etkin was withdraw and entered a new rejection based on Lascelle in combination with Michael, addressing the new amended claim language.
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.
Claim(s) 1-3, 9-11, 17-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable by WO2021173571 – Lascelles in combination with US20230123527-Michael.
Claim 1. Lascelles teaches, A processor executed method for predicting diseases in animals, comprising: (Lascelles, [0011-0012], [ 0006-0008], abstract) Lascelles teaches a "computer-implemented method" run on a "computing device" or "processing unit" using software for "evaluating a movement-related condition" (like OA) "in an animal" (specifically mentioning dogs and cats), which results in a "predicted movement score indicating the movement-related condition". Lascelles discloses a processor-executed method (computer-implemented on processing unit) for predicting/evaluating a disease/condition (OA/movement condition) in animals (dogs/cats). The Lascelles receiving animal-related data and predicting a movement-related condition via a machine learning model.
receiving patient medical record data; (Lascelles, [0006], [0011], abstract) Lascelles teaches the processing unit "receiving sensor data" (measuring movement parameters indicative of health/condition) electronically from sensors associated with the animal.
filtering the received patient medical record data by at least one of species, breed, gender, or geographic location; (Lascelles, [0019], [0093], [0035]- [0036], [0065]- [0066], 0072]) Lascelles teaches receiving data relevant to the animal patient. While Lascelles primarily focuses on receiving "sensor data", analogous to "patient medical record data" needed for context and model training/application under BRI. Lascelles discusses selecting subjects for training based on "sexes, sizes, weights, breeds, and detailed health phenotypes" and creating profiles including "breed, sex, age, and name of the subject", which constitute patient medical record data.
separating the filtered patient medical record data into first structured data and unstructured data, wherein the first structured data includes at least one or more diagnostic test results and the unstructured data(Lascelles, p. [0003-0004], [0006], [0012], [0072], [0074, [0019], [0066]- [0067], [0085], [0093]) Lascelles receives/slicing and processes raw sensor data (unstructured) and uses metadata be appended (structured). Lascelles teaches receiving "sensor data" (e.g., accelerometer, gyroscope readings) which constitutes unstructured data representing continuous measurements over time. Lascelles also utilizes "metadata associated with the subject" such as "pathology, activity type, repeat number", "breed, sex, age" which constitutes structured data. The overall process involves processing the raw, time-series sensor data (unstructured) and associating it with discrete metadata attributes (structured) for training and prediction.
Prior art disclosed the evaluation of clinical signs, owner observations, and general health information collected by veterinary specialists to define an animal's phenotype.
, includes data includes clinical (Lascelles, p. [0006], [0012], [0072], [0074, [0019], [0066]- [0067]])
Lascelles also utilizes "metadata associated with the subject" such as "pathology, activity type, repeat number", "breed, sex, age" which constitutes structured data.
Lascelles addresses the existence of clinical information, as said this is read on "clinical signs" and "information collected by veterinary specialists" because the reference recognizes that professional medical observations and owner-reported symptoms are the standard basis for identifying health conditions like osteoarthritis.
training a first neural network on the unstructured data as a first training set to extract second structured data from the unstructured data by using (Lascelles, [0068-0070], 0088-0089, [0100] [0006])
The reference explicitly names neural networks as a potential model and describes training it on sensor measurements. The trained CNN processes the sensor data to classify it, producing a specific, structured label like "walking" or "trotting."
the ontology defining the second structured data; (Lascelles, adaptive weights par. 0068, normalizes the calculated metrics values par. 0088; dataset is clean and normalized par. 0089)
combining the first structured data and the second structured data to form a second training set for training a second neural network; (Lascelles, par. 0085-0089[0074-0075], [0090])
Lascelles disclosed the combination of multiple sets of structured data to form a training set for a second machine learning model, as said this is read on combining calculated kinematic movement metrics, identified activity types, and associated subject metadata into a tabular training dataset utilized to train the movement condition model, which can be an artificial neural network.
wherein the combining includes using date and timestamp information in the patient medical record data to associate, (Lascelles, sensor data may be time-stamped par. 0066; the sensor data collected by the sensing unit 106 may be time stamped par. 0101; executing in a veterinary hospital or clinic, or on the cloud par. 0038; movement score, in certain embodiments, the presently disclosed diagnostic system determines certain metrics par. 0031; The processing unit 106 is configured to maintain a baseline movement model and to process sensor data received from the sensing unit 104 to determine a movement score par. 0038; Fig. 6; appended to the sliced sensor data par. 0074; dataset includes the various metrics values and activity types and the corresponding movement scores par. 0089; par. 0107; mapping function between the input variables/features (metrics values in this case) and an output variable (the movement score in this case) par. 0089-0090)
training the second neural network using supervised learning on the second training set including the plurality of input feature vectors formed from the combined first structured data and second structured data to output the likelihood of one or more diseases; (Lascelles, [0006], [0008], [0031], [0087-0089], [0090-0091], [0109], [0090])
Lascelles explicitly teaches that the "movement condition model" (the second model) can be an artificial neural network (ANN) that is trained on a combined dataset. This trained model outputs a "predicted movement score" that indicates a "movement-related condition" (e.g., Osteoarthritis), which corresponds to the likelihood of a disease. The score differentiates between a healthy animal and one with a condition, thereby assessing the likelihood of disease.
applying the trained first neural network and the trained second neural network, in sequence, on new patient medical record data to predict disease diagnosis, wherein a first error function wherein the first neural network and the second neural network constitute a two-stage machine learning architecture that enables effective (Lascelles, [0010], [0006], [0068-0070],[0089-0093], [0098], [0100-0101], [0103], [0105], [0106], [0109-0110])
Lascelles, describe a two-step process where a first neural network identifies activities from sensor data, and a second neural network uses metrics from those activities to predict a disease condition.
Lascelle describe error function because said if the output is incorrect, the CNN changes its weight to be more likely to produce the correct output.
A POSITA would, prior to the filing date, modify the primary reference Lascelles that describes separating patient data into structured and unstructured formats including diagnostic information, as shown by appending "metadata... [such as] pathology, activity type" to raw unstructured sensor data and utilizing "information collected by veterinary specialists to define the phenotype" (Lascelles, Para. [0074], [0085], [0034], [0040], [0100], [0105], [0109]), which does not precisely describe the unstructured data includes free-form text or segmentation of words or phrases, with a comparable secondary reference Michael that describes system includes a data-ingestion and data-consolidation architecture, using sensor data and ontology technique to generate ontology structured data by extracted set of words from nonstructured data (Michael, par. 0013-0014, 0076-0077, 0079-0082) , in order to diagnosing and understanding the problem and make it easy to query, analyze, and integrate with other structured data source without altering the purpose of the primary reference.
A POSITA would, prior to the filing date, modify the primary reference Lascelles that describes error function in supervised machine learning par. 0068-0070, 0085, 0087, 0089-0090, 0092, 0109 which does not precisely describe unsupervised error function in combination with the first error function, with a comparable secondary reference Michael that describes unsupervised learning first stage error functions different from the error function of the supervised learning stage, using mimic output to correct themselves and backpropagation for second stage using supervised learning (Michael, 0082-0094), in order to minimized by adjusting weights and biases (Michael, par. 0093-0095) produced the predictable result of use Michael unsupervised first-stage neural network to mimic its input data and minimize a first error function, while using Lascelles supervised condition model to minimize a different second error function between each multi-feature input vectors ground-true health/condition label and the model’s predicted output without altering the purpose of the primary reference.
Claim 2.
Lascelles in combination with Michael teaches,
The method according to claim 1, wherein the second training set for the second neural network includes one or more input features extracted from the combined first structured data and second structured data and corresponding ground truth. (Lascelles, paragraphs [0074-0075], [0084- 0085], [0089], [0090], [0106], [0109], [0118]) Lascelles describes calculating specific "movement-related metrics" (input features) from processed sensor data slices. This derived structured data (metrics) is explicitly combined with "corresponding metadata associated with the subject" (e.g., pathology, breed, age - original structured data) to form the "training data set". This training set, comprising the input features derived from the combined data and linked to manually computed "movement scores" (corresponding ground truth), is used to train a supervised "movement condition model" (second machine learning model).
Claim 3.
Lascelles in combination with Michael teaches,
The method according to claim 2, wherein the one or more input features include one or more of an age of the patient, propensity of the patient to one or more diseases, one or more test results, one or more symptoms, and one or more observations, and wherein the ground truth includes the likelihood of one or more diseases. (Lascelles, [0006], [0075], [0085], [0087], [0089], [0092-0094])
Lascelles teaches using calculated "movement-related metrics" derived from sensors, which
constitute "observations", as input features. Lascelles also includes "metadata associated with
the subject" in the training data, which may include "age" and "breed", potentially indicating
"propensity" and including "pathology" (analogues to diseases), metadata suggests prior conditions might be used. Lascelles uses a manually computed "movement score" as ground truth. This score evaluates a "movement-related condition" (e.g., OA) and distinguishes between having the "condition" and being "healthy. The movement score based on specialist assessment of phenotype thus represents the likelihood or classification of a disease/condition.
Claim 18
Lascelles in combination with Michael teaches,
The method according to claim 1, wherein training the first neural network on the unstructured data as the first training set further includes using the unsupervised learning to perform natural language processing comprising at least one of word frequency counting, dependency parsing, context tracing, or part-of-speech tagging to segment the unstructured data into a plurality of words or phrases.
Under the broadest reasonable interpretation, this limitation requires utilizing unsupervised machine learning and natural language processing, such as word frequency counting, to segment and process an unstructured text corpus to train a neural network.
Lascelles teaches training a neural network on a training set, as shown in (Paragraphs [0008], [0068]). This reads on training the first neural network on a training set because it explicitly discloses configuring and optimizing artificial neural network models using collected data sets.
However, Lascelles does not teach using unstructured data as the training set, nor does it teach using unsupervised learning to perform natural language processing comprising word frequency counting to segment the unstructured data.
A POSITA would, prior to the filing date, modify the primary reference Lascelles, with a comparable secondary reference Michael that describes performing techniques such as part of speech tagging par. 0080, in order to identify the salient semantic-based words and phrases making more easy to query, analyze and integrate with other structured data source (par. 0076 and 0080) without altering the purpose of the primary reference.
Note: Claims 9-11, and 17 are rejected with the same analysis above.
Claim(s) 6,7, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable by WO2021173571 – Lascelles in combination with US20230123527-Michael in further view of US20190362846A1 - VODENCAREVIC.
Claim 6.
Lascelles in combination with Michael teaches,
The method according to claim 1, further including: training a plurality of second neural networks; (Lascelles, paragraphs [0008], [0068], [0072], [0075], [0089-0090]).
Lascelles teaches training machine learning models (e.g., SVM, CNNs) using supervised learning on structured metrics derived from sensor data (unstructured time-series data) to predict movement scores indicative of a health condition. Lascelles explicitly mentions training "two different CNNs" in one embodiment and describes distinct activity and movement base models, thus teaching the training of a plurality (two or more) models within its system.
evaluating the plurality of second neural networks using one or more metrics; Lascelles, paragraphs [0008], [0068], [0072], [0075], [0089]).
and selecting one or more neural networks of the plurality of second neural networks for application to the new patient medical record data to predict disease diagnosis. (Lascelles, paragraphs [0090], [0093], [0103], [0109], [0112], [0008], [0068], [0072], [0075], [0089]).
Lascelles clearly teaches applying its trained movement base model to new sensor data ("new records") from a subject to predict a movement score indicating a health condition. Lascelles discloses training a "movement base model" using supervised learning ("supervised machine learning algorithm") on "movement-related metrics" from "sensor data" to predict "movement scores" [Lascelles, paragraphs [0006-0008], [0065], [0075], [0089], [0090]] and teaches training multiple models such as "two different CNNs" or distinct "activity model" and "movement base model" [Lascelles, paragraphs, [0056], [0068], [0072]].
Lascelles discusses model training until models correctly predict output "most of the times" [Lascelles, paragraph 0070-0071] and applying the trained model [Lascelles, paragraph 0090, paragraph 0109], but does not describe evaluating multiple trained base models against specific metrics like error rate or complexity, or selecting based on such evaluation.
However, Vodencarevic describes performing "nested k-fold cross-validation procedure ... the inner one which tunes the hyperparameters and the outer one which estimates the performance" (Vodencarevic, paragraph [0018]), using "performance metrics... such as the Area Under the receiver operating Characteristics (AUC) or classification error" (Vodencarevic, paragraph [0017], comparing "averaged results... for different models... and finally the one that maximized the model performance is selected as the best model" (Vodencarevic, paragraph [0017]), and comparing "aggregated testing results... for different trained models and selecting the optimal predictive model".
Combining Lascelles and Vodencarevic would have been obvious under 35 U.S.C. 103 because both references operate within the analogous art of developing and optimizing predictive machine learning models that process input data to generate health-related assessments. Lascelles focuses on predicting animal movement scores from sensor data ("evaluating a movement-related condition") [Lascelles 0006-0008, 0026, 0030], while Vodencarevic focuses on automated clinical decision support using EMR data, specifically including model evaluation and selection ("creating predictive models", "model selection") [Vodencarevic paragraphs, 0002, 0009, 0017, 0018, 0111, 0114]; they share the technical goal of building effective ML systems for health assessment. Vodencarevic teaches methods for "automated optimal model and parameter selection" (Vodencarevic, paragraph [0398]) which allows selection of the model that "maximized the model performance" (Vodencarevic, paragraph [0017]) and provides a "conservative model performance estimation" (Vodencarevic, paragraphs [0197]), benefits readily understood by one skilled in the art as valuable for improving any predictive system like that in Lascelles which aims to accurately evaluate health conditions.
Claim 7.
Lascelles in combination with Michael teaches,
The method according to claim 6, wherein the one or more metrics include prediction error, complexity, explainability, or data size. (Vodencarevic, paragraphs [0017], [6101], [0206], [(0436}).
Note: Claims 14-15 are rejected with the same analysis above.
Claim 21.
Lascelles in combination with Michael teaches,
The method according to claim 1, wherein the first neural network comprises a convolutional neural network trained error function that expresses error as a low probability that erroneous output occurs or as an unstable high energy state in the convolutional neural network, and wherein the first neural network adjusts weights and
Under the broadest reasonable interpretation, this limitation requires minimizing classification error probabilities by updating both connection weights and node biases during training.
Lascelles teaches a convolutional neural network that adjusts its weights in response to incorrect outputs to reduce error. However, Lascelles does not teach adjusting biases for each connected pair of neurons based on the error.
Michael supply the training input - output error- correction of weight and biases for each connected neuron pair - continuing adjustment as additional data is input in par. 0082 and 0094.
A person of ordinary skill in the art would have combined Lascelles' CNN to adjust "bias weights" alongside connection weights par. 0068 with Michaels models par. 0082 and 0094 for the objective accuracy improvement.
Lascelles in combination with Michael teaches, Claim 22.
The method according to claim 1, wherein training the second neural network comprises determining a minimum value of the second error function in weight space, and wherein weights and . (Lascelles, 0068, 0070-0071, 0089, 0098, 0101)
Lascelles describes predictive-model framework because.
However does not exactly describes strikethrough line above.
Michael describe the missing elements above because describe supervised second-stage neural network, backpropagation, minim value of the error function in weight space using gradient descent in repeatedly adjustment for each layer, starting with the output layer par. 0094, 0082.
A POSITA would, prior to the filing date, modify the primary reference predictive-model framework, with the comparable secondary reference neural network training, in order to works better at finding the optimal model, without overfitting without altering the purpose of the primary reference.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/J.D.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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