DETAILED ACTION
This non-final office action is responsive to application 18/431,233 with applicant’s amendments and request for reconsideration as submitted 17 Nov. 2025.
Claim status is currently pending and under examination for claims 1-20 of which independent claims are 1, 8, and 15 further corresponding to the amended claims; no claims are currently in condition for allowance.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/17/2025 has been entered.
Response to Remarks
Examiner thanks applicant for responsive remarks filed 11/17/2025 which are considered together with the amendments on all outstanding issues as follows:
The rejection under 35 U.S.C. 112(a) as lacking written description is withdrawn as necessitated by amendment striking the affected language.
Rejection under 35 U.S.C. 101 as being directed to an abstract idea without significantly more is hereby maintained. Applicant’s traversal is not found persuasive for the following reasons.
Applicant points to the amendments and contends that the analysis step is advanced such that it cannot be practically performed by the human mind and uses data of high complexity. However, examiner respectfully disagrees pointing out that the amendments concerning machine learning are not treated as part of the abstract idea. Evidence is provided to further show that the functionality is known, particularly reference Pineda at Fig 1 revealing that the limitation does not amount to inventive concept. Cross-domain health data integration by domain adaptation demonstrates that a machine learning nexus between human and veterinary domains is a prior discovery. The machine learning as a whole merely serves as an apply-it step to the analyze limitation which can be seen as a medical diagnosis for catch-all cancer and/or chronic any-disease in the case of claim 15. When read in light of the instant specification, described are veterinary practitioners whom would serve to perform such cognitive tasks as daily routine in an ordinary capacity. Further additional elements of transmit and receive with display user interface are conventional computer functions that are identified as well-understood, routine and conventional as per MPEP 2106.05(d). Looking at the limitations as an ordered combination does not impose constraints of a sequential or temporal relevance the elevate the claim as a whole beyond the conventional system for implementing the abstract idea. Accordingly, the balance of the evidence supports a finding of ineligibility and the rejection is maintained.
Applicant’s remarks regarding the prior art have been considered, but are moot in view of the new grounds of rejection as necessitated by applicant’s amendments. Updated search and consideration identifies additional prior art to meet the scope of claim as amended. Particularly, the newly applied references include Pineda and Chorny, of which Pineda is principally found to teach or suggest the analyze limitation when fairly considered as a whole, especially in light of remarks noting cross-domain and multi-domain correlation which Pineda’s refers to as domain adaptation. As an additional matter, acknowledgement is made to the priority date – however, this appears to rest on the term cancer which is not used in the independent claim 15 instead using natural language processing which is only found in the most recent application. Therefore, priority date is inconsistent and Farrell’s PetBERT qualifies as a prior art. Regardless, the rejection now stands in light of Pineda which the examiner respectfully submits to make of record in an updated finding of obviousness detailed below.
Claim Objections
Claims 1 and 15 are objected to because of the following informalities: limitation recites “determine a health topic based on the captured data” where captured should be “collected” data as preceded by the prior limitation to properly introduce the term, similar to correct form of claim 8. Appropriate correction is required.
Claims 6 and 13 are objected to because of the following informalities: limitation recites “the relevant product” should be “a relevant product” so as to properly introduce the term. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within one of the four statutory categories: claims 1-7 and 15-20 system/machine, and claims 8-14 are a method/process. Therefore, the analysis proceeds per MPEP 2106.03
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the enumerated grouping of abstract idea being “Mental Processes” but for the recitation of generic computer components. In particular, claims recite:
Claims 1 and 8
“collect data corresponding to a pet and a human owner of the pet” (mental observation or collected by hand as in filing patient charts)
“determine a health topic based on the capture data” (mental determination)
“analyze the answers to predict a condition of the animal based on the captured data, […], wherein said predicting further includes comparing a medication of the human owner to the plurality of categories and determining the corresponding correlation by the model for a probabilistic determination of an initial onset of cancer” (medical opinion or evaluation by estimation)
“determine a course of action based on the predicted condition of the animal, wherein the course of action is a recommendation comprising at least one of an at-home treatment, connecting the human owner with a veterinarian, and transporting the animal to a clinic” (mental determination or judgment of medical practitioner)
Claim 15 difference in scope comprises
“analyze the answers to predict a condition for the animal based on the captured data, […], wherein said predicting further includes comparing the geographical information of the human owner to the plurality of categories and determining the corresponding correlation by the model for a probabilistic determination of an initial onset of a chronic disease” (medical opinion or evaluation by estimation)
Focus of the claim concerns animal health decision support based on determining and analyzing. When read in light of the specification, veterinarians are described. Such veterinarian is reasonably a human whom provides medical opinion upon evaluating companion animals, diagnosing and prescribing treatments as a business interaction. Because these limitations may be performed in the mind, they are considered a mental process. Therefore, the claims are drawn to mental processes as the abstract idea.
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
“a processor; and a non-volatile, non-transitory memory storing a module with instruction executed by processor” and “memory” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform abstract idea
“transmit a question set corresponding to the health topic, the question set including one or more questions, wherein at least one question of said question set corresponds to a medical history associated with the human owner” & “receive answers associated with the question set” MPEP2106.05(g) adding insignificant extra-solution activity to the judicial exception
“wherein the predicting comprises training a machine learning model… wherein the machine learning model assigns the collected data, the health topic, and the medical history to a plurality of points in space, wherein said machine learning model further groups sets of points of the plurality of points in a plurality of categories, wherein said machine learning model further determines a correlation between two or more categories” and claim 15 “wherein the predicting comprises training a natural language processing model based on data stored in memory… wherein said natural language processing model assigns the collected data, the health topic, and the medical history to a plurality of points in space, wherein said natural language processing model further groups sets of points of the plurality of points into a plurality of categories, wherein said natural language processing model further determines a correlation between two or more categories” MPEP 2106.05(h) generally linking the use of the judicial exception to a particular technological environment or field of use
“generate the course of action for display on a user interface, wherein the user interface further includes one or more selectable options associated with the course of action corresponding to the predicted condition of the animal” MPEP2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting
Balance of the claim concerns general computer elements, transmitting and receiving data, trained ML/NLP models, and display output. The aforementioned additional elements are recited at a high level of generality and do not meaningfully limit the claim. As set forth per MPEP 2106.04(a)(2) “A claim that requires a computer may still recite a mental process”. Accordingly, the claim remains drawn to mental processes and the additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and do not reveal an inventive concept. The additional elements are as follows:
“a processor; and a non-volatile, non-transitory memory storing a module with instruction executed by processor” and “memory” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform abstract idea. Particularly, a general purpose computer that applies a judicial exception by use of conventional computer elements does not qualify as a particular machine MPEP 2106.05(b)
“transmit a question set corresponding to the health topic, the question set including one or more questions, wherein at least one question of said question set corresponds to a medical history associated with the human owner” & “receive answers associated with the question set” MPEP2106.05(g) adding insignificant extra-solution activity to the judicial exception. Particularly, said extra-solution activity is a well-understood, routine and conventional (WURC) activity per MPEP 2106.05(d)(II)(i) “The courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: i. Receiving or transmitting data over a network, e.g. using the Internet”
“wherein the predicting comprises training a machine learning model… wherein the machine learning model assigns the collected data, the health topic, and the medical history to a plurality of points in space, wherein said machine learning model further groups sets of points of the plurality of points in a plurality of categories, wherein said machine learning model further determines a correlation between two or more categories” and claim 15 “wherein the predicting comprises training a natural language processing model based on data stored in memory… wherein said natural language processing model assigns the collected data, the health topic, and the medical history to a plurality of points in space, wherein said natural language processing model further groups sets of points of the plurality of points into a plurality of categories, wherein said natural language processing model further determines a correlation between two or more categories” MPEP 2106.05(h) generally linking the use of the judicial exception to a particular technological environment or field of use. Particularly, the trained models do not meaningfully limit the claim as it does not satisfy the test of particular transformation MPEP 2106.05(c)(e). Supplemental evidence demonstrates such functionality being known as per Pineda et al “FasTag: automatic text classification of unstructured medical narratives” at Fig 1.
“generate the course of action for display on a user interface, wherein the user interface further includes one or more selectable options associated with the course of action corresponding to the predicted condition of the animal” MPEP2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting. Particularly, said extra-solution is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(iv) “types of activities that the courts have found to be well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: iv. Presenting offers” and/or per MPEP 2106.05(h) “displaying certain results of the collection and analysis”
Significantly more is not apparent from the balance of the claim. If the claim language provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it, then the claims do contain an inventive concept. Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). 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.
In view of the foregoing, the claims are not patent eligible. This rejection applies equally to independent claims 1, 8 and 15 as well to dependent claims 2-7, 9-14 and 16-20. Dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea or that they include additional elements which integrate the judicial exception into a practical application or amount to significantly more.
Dependent claims 2, 9 and 16 disclose a severity level of predicted condition and wherein the model performs natural language processing. A severity level is considered part of the abstract idea being a mental evaluation, and the model being NLP is already addressed as in claim 15 which amounts to generally linking the use of the judicial exception to a particular technological environment of field of use per MPEP 2106.05(h) and which fails to meaningfully limit the claim per MPEP 2106.05(e). As of the effective filing date ~2024, machine learning models have progressed well into the age of GPT and LLM which renders NLP conventional.
Dependent claims 3 and 10 disclose limitations further operative to access memory comprising questions and obtain question set based on symptoms. The limitations are considered additional elements which amount to adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g), e.g. mere data gathering. Particularly, said extra-solution activity is a WURC activity per MPEP 2106.05(d)(II)(iv) “The courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: iv. Storing and retrieving information in memory”
Dependent claims 4-5, 11-12 and 18-19 disclose calculating a severity score and comparing it to predetermined thresholds. This is considered part of the abstract idea being mental evaluation or simplistic math. For example, pain on the 10-scale or a tiered low, medium and high risk factors. There are no additional elements.
Dependent claims 6, 13 and 20 disclose producing at least one in the alternative comprising medication option, directions to a veterinarian, and a hyperlink. The medication option and directions to a veterinarian are considered part of the abstract idea where medication is by medical opinion.
Dependent claims 7 and 14 disclose storing course of action and date of recommendation. This is considered and additional element which amounts to adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(g), e.g. mere data gathering. Particularly, said extra-solution activity is a WURC activity per MPEP 2106.05(d)(II)(iv) “The courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: iv. Storing and retrieving information in memory”
Dependent claim 17 discloses wherein the chronic disease is at least one of cancer, renal failure, lung disease, and kidney failure. This is considered part of the abstract idea being mental process of medical opinion for diagnosing a medical condition. There are no additional elements.
Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). 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.
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 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.
Claims 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Shuler et al., US PG Pub No 20150039239A1 hereinafter Shuler, as is evidenced by Provisional 61/860,512 in view of
Olives et al., US Patent No 10,930,401B2 hereinafter Olives, in view of
Chorny et al., US PG Pub No 2021/0247393A1 hereinafter Chorny as evidenced by Provisional 62/909,625, in view of
Pineda et al., “FasTag: automatic text classification of unstructured medical narratives” hereinafter Pineda (bioRxiv: 10.1101/429720).
With respect to claim 1, Shuler teaches:
A system for providing animal health decision support {Shuler discloses [0109] “system for remotely monitoring the health state of at least one non-human animal” Figs 1-4. Corresponding provisional support includes [0012,14]. Figs 1-4}, the system comprising:
a processor {Shuler Figs 3:205 and/or 4:280 “Processor” described [0025-26].
Corresponding provisional support at Figs 3:205 and/or 4:280, [0081]}}; and
a non-volatile, non-transitory memory storing a module with instruction executed by the processor, the processor {Shuler Figs 3:210 and/or 4:285 “Memory”, [0038] “computer device 202 includes a processor 205 for executing instructions stored in a memory 210”. Corresponding provisional support at Figs 3:210 and/or 4:285, [0082]} operative to:
collect data corresponding to a pet and a human owner of the pet {Shuler discloses [0055] “Data collection system 300” shown Fig 8:300 with companion animal and care provider, e.g. [0089,95] “data signatures can be collected for certain dog breeds… animal owners” and “data collection system also includes a user application configured to receive and transmit observed data that is input to the user application by a user”. Corresponding provisional support includes Fig 8, [0046], [0072,78]};
determine a health topic based on the captured data {Shuler [0071-72] “determine a wellness or behavioral change of the animal base on reference health and physiological state profiles” i.e. [0023] “individualized behavioral/health state profiles” profile is a topic, e.g. [0064-66] “health, nutrition, and physiological state profiles that are based on previously collected data that is stored on database 310 for the individual animal… profiles that indicate at least one of the following health, physiological, or behavioral states: healthy, pain, estrus, rumination, reduced mobility, birthing, anxiety, ear infection, medication side effects, body weight fluctuation, bodily function events, and food/water intake”. See also [0075] “each profile… user may select which of the health assessment profiles is to be utilized”. Corresponding provisional support includes [0054-59], [0022] [0062]};
Shuler further discloses predictive diagnosis with analysis, as well as receiving and transmitting with smartphone and display user interface to provide recommendations generated based on some course of action items.
However, Shuler does not appear to fairly disclose the following limitations which are met by Olives:
transmit a question set corresponding to the health topic, the question set including one or more questions {Olives [Col4 Line63 – Col5] “questions will be selected… pet owner is asked a question” or [Col7 Line17] “follow-up symptoms area allows questions to be specified to be asked of an owner” shown Fig 2 where symptoms convey health topic, further shown Figs 1-7, transmit is communication shown Fig 8 which may include a smartphone [Col7 Line23], [Col8 Line49]},
receive answers associated with the question set {Olives discloses [Col4 Line63 – Col5] “answers to this question… answer options offered for the symptom” result reported Fig 3};
determine a course of action based on the predicted condition of the animal, wherein the course of action is a recommendation comprising at least one of an at-home treatment, connecting the human owner with a veterinarian, and transporting the animal to a clinic {Olives [Col4 Lines1-24] “Fig.3 …action, ‘Take action’ tab 306 provides a recommendation 307 (e.g., contact clinic right away), a button to call the clinic” determining includes [Col3 Lines10-16] “triage categories may include emergency, urgent, seek medical advice, non-threatening, and so on. The PET system then provides a recommendation based on the triage category. For example, when the triage category is emergency, the recommendation may be to take the pet to a clinic immediately” introduced [Col2 Lines13-34], see also Fig 9:903-904}; and
generate the course of action for display on a user interface, wherein the user interface further includes one or more selectable options associated with the course of action corresponding to the predicted condition of the animal {Olives Fig 3 screenshot “Take Action” described [Col4 Lines1-24] “display page 300” tabs and buttons are selectable options, further described [Col4 Lines62-64], similar [Col9 Lines1-57] “display the triage recommendation” Figs 9:903-904 which can be estimated as per [Col6 Lines11-13]. Implementation may employ [Col7 Lines22-37] “smartphones… interfacing with the PET system or a web browser”}.
Olives is directed to animal health systems thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ the teachings of Olives in combination with Shuler’s a motivation of addressing the situation that “Most pet owners, however, cannot effectively triage a current condition of a pet to know whether they should seek emergency attention for their pet, call a veterinarian to discuss the pet’s condition, wait and see how the condition progresses, and so on. Moreover, the urgency of seeking medical attention for a condition may vary based on the species, breed, age, and so on of the pet. This variation makes it very difficult for the vast majority of pet owners to effectively triage a current condition. As a result, many pets are taken to a clinic even when not warranted by the condition. Conversely, many pets are not taken to a clinic even when warranted by the condition sometimes with dire consequences” [Col1 Lines18-30].
However, the combination of Shuler and Olives does not disclose the following limitation which is met by Chorny:
wherein at least one question of said question set corresponds to at least one of financial information and geographical information associated with the human owner {Chorny [0103,05] “prompt 505, the consumer entity is asked to enter in location (i.e., home address of the consumer entity)” describes Fig 5A interface, the location/address is geographical information. Corresponding provisional support comprises [0102, 104], Fig 5A};
Chorny is directed to cancer detection for veterinary health assessment thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include questions about location per Chorny with the question-answering of Olives in combination for a motivation “this would be a convenience for consumer entities residing in rural and isolated areas or if the consumer entity or pet are not health enough for travel to the clinical entity” [0104] and/or “give the consumer options for veterinarians, based on the geographic location” [0039]. Corresponding provisional support comprises [0103], [0038].
However, the combination Shuler, Olives and Chorny does not fairly teach or suggest the following limitation as a whole which is met by Pineda:
analyze the answers to predict a condition of the animal based on the captured data, wherein the predicting comprises training a machine learning model based on information stored in said memory corresponding to the health topic and the medical history associated with the human owner, wherein said machine learning model assigns the collected data, the health topic, and the medical history to a plurality of points in space, wherein said machine learning model further groups sets of points of the plurality of points into a plurality of categories, wherein said machine learning model further determines a correlation between two or more categories, wherein said predicting further includes comparing a medication of the human owner to the plurality of categories and determining the corresponding correlation by the model for a probabilistic determination of an initial onset of cancer; {Pineda Fig 1 reproduced below, illustrates Evaluation (Analysis) using trained LSTM models for predictive tagging/classifying over “cross-species” databases Veterinary (CSU) and Human (MIMIC-III) [P.5-6] together in combination, tag/label regards “multi-label classification” [P.7-9] where categories of disease/cancer listed Table 1 [P.6], noting “comparative oncology” [P.3,14] oncology is cancer study and medications or prescriptions are disclosed [P.7]. Importantly, “domain adaptation” [P.9] provides the correlation of cross-species between human and veterinary domains as a generalization technique. Vector space is used for word embeddings of a clinical narrative [P.7] with featurization performed by statistic analysis of tf-idf that reflects the importance of words to be mapped [P.8], example at Table 2. The reference is relevant in its entirety, and is similarly shown in same author’s earlier work being titled “Deep learning facilitates rapid classification of human and veterinary clinical narratives”}
Pineda is directed to veterinary cohort models with machine learning thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to analyze according to the teachings of Pineda in combination for a motivation “Automatically adding meaningful disease-related tags to human and veterinary clinical notes using the same machinery would be a huge step forward in that exploration and could facilitate cross-species findings downstream” and “improve both veterinary and human coding accuracy as well as comparative analyses across species… improved cohort discovery through automated record tagging” [P.3 ¶1-2], [P.14 ¶4].
PNG
media_image1.png
1172
862
media_image1.png
Greyscale
With respect to claim 2, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 1, wherein
the machine learning model is a natural language processing model, said natural language model configured to output a severity level corresponding to the predicted condition of the animal {Pineda [P.8 ¶4] “NLP” is natural language processing, introduced [P.3 ¶1] and uses word embeddings with LSTM [P.7 ¶1]. Further, [P.14 ¶4] “stratifying patients by specific diseases, severities” severities are considered and which may entail scoring [P.3 ¶2]}.
With respect to claim 3, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 2, wherein the processor is further operative to:
access the memory comprising one or more questions sets; and obtain the question set based on the one or more symptoms associated with the animal {Olives Fig 2 illustrates the questions based on symptoms of the pet, and discloses memory [Col8 Line25] for implementing the computer system which performs question answering [Col4 Line63]}.
With respect to claim 4, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 1. Olives teaches wherein the analyze step further comprises
calculating a severity score based on the received answers to the question set, wherein each answer is associated with a score {Olives [Col5 Lines10-62] “Fig. 6 illustrates specification of a 1-10 scale point option. The symptoms may include… Extreme Discomfort=9” Fig 6 shows 1-10 scale (severity) for the symptom questioning Fig 2, analysis [Col6 Lines9-20] “weights may take a value between 1 and 10 and are specific to the chief complaint… weight associated with the symptom”}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to include severity scoring per Olives as applying a known technique to a known method ready for improvement to yield predictable results and/or for a motivation of weighting symptoms according to a 1-10 scale commonly used for rating pain levels or the like.
With respect to claim 5, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 4, wherein the processor is further operative to
compare the severity score to one or more predetermined thresholds, each predetermined threshold associated with one or more courses of action {Olives [Col6 Lines16-58] “maximum value of the input scale value for symptom i …Recommendation=maxiPr(∙)” also [Col10 Lines22-24] “minimum for scale point filter to identify the minimum categorization based on the scale points of the symptoms” maximum and/or minimum are threshold comparisons for the data scored according to scale and associated with the courses of action Fig 3}.
With respect to claim 6, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 1, wherein
the one or more selectable options correspond to at least one of a medication option, directions to a veterinarian, or a hyperlink to the relevant product corresponding to the determined course of action {Olives Fig 3 shows address of animal hospital with buttons to call or consult conveys directions to a veterinarian, further describing client-side app or web pages accessible by web browser to convey hyperlinks [Col7 Lines35-42]}.
With respect to claim 8, the rejection of claim 1 is incorporated. The difference in scope being a method to perform limitations of system claim 1. Shuler discloses [0013] “methods” for described techniques. The remainder of this claim is rejected for the rationale applied to claim 1.
With respect to claim 9, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 8, and further teaches the limitation of claim 2. Therefore, the rejection of claim 2 is applied to claim 9.
With respect to claim 10, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 9, and further teaches the limitation of claim 3. Therefore, the rejection of claim 3 is applied to claim 10.
With respect to claim 11, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 8, and further teaches the limitation of claim 4. Therefore, the rejection of claim 4 is applied to claim 11.
With respect to claim 12, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 10, and further teaches the limitation of claim 5. Therefore, the rejection of claim 5 is applied to claim 12.
With respect to claim 13, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 8, and further teaches the limitation of claim 6. Therefore, the rejection of claim 6 is applied to claim 13.
With respect to claim 15, the rejection of claim 1 is incorporated. The difference in scope being a system to perform limitations similar to claim 1, with the analysis including natural language processing. The system is established by Shuler and the further natural language processing is met by Pineda [P.5 ¶3] “NLP tool” introduced as natural language processing [P.3 ¶1] and uses word embeddings [P.7 ¶1]. The motivation remains equally applied, and the remainder of the claim is rejected for the rationale applied to claim 1.
With respect to claim 16, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 15, and further teaches the limitation of claim 2. Therefore, the rejection of claim 2 is applied to claim 16.
With respect to claim 17, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 15, wherein
said chronic disease consists of at least one of cancer, renal failure, lung disease, and kidney failure {Pineda [P.14 ¶4] “oncological cases, which our model performed well on” oncology i.e. cancer, introduced [P.2-3 ¶4] “cancer registries… pathology data to identify helpful traits like species-specific cancer resistance”}.
With respect to claim 18, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 15, and further teaches the limitation of claim 4. Therefore, the rejection of claim 4 is applied to claim 18.
With respect to claim 19, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 18, and further teaches the limitation of claim 5. Therefore, the rejection of claim 5 is applied to claim 19.
With respect to claim 20, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 15, and further teaches the limitation of claim 6. Therefore, the rejection of claim 6 is applied to claim 20.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Shuler, Olives, Chorny and Pineda in view of Bradley et al., PCT WO2020/018463A1 hereinafter Bradley.
With respect to claim 7, the combination of Shuler, Olives, Chorny and Pineda teaches the system of claim 1. Bradley teaches wherein the processor is further operative to
store the course of action and date of the recommendation {Bradley [P.67 Line2] “history as recorded in the veterinary database” where [P.101 Lines13-14] “Data points were primarily collected during or around hospital visits, with individual visits timestamped” [P.76 Lines18-30] “For a cat with N visits, its trajectory was defined as the temporally ordered list of visits” see e.g. Figs 10, 26}.
Bradley is directed to animal health assessment of chronic kidney disease with neural networks thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to store patient records (a common legal requirement for malpractice) along with the course of actions performed and recommendations timestamped according to Bradley in combination to arrive at the invention as claimed as applying a known method to a known technique ready for improvement to yield predictable results and/or for a motivation to model patient trajectory over time [P.76 Lines18-25].
With respect to claim 14, the combination of Shuler, Olives, Chorny and Pineda teaches the method of claim 8, and further combination with Bradley teaches the limitation of claim 7. Therefore, the rejection of claim 7 with equal motivation is applied to claim 14.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Modiano et al., US PG Pub No 2022/0252602A1 see Fig 23 cancer prediction for canine
Farrell et al., “Natural Language Processing for Forecasting Mortality and Premature Death in Companion Animals” Fig 5 PetBERT transformer model ~2023
Zhang et al., “Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding” see Fig 1, NeurIPS 2018
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chase P Hinckley whose telephone number is (571)272-7935. The examiner can normally be reached M-F 9:00 - 5:00.
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, Miranda M. Huang can be reached at 571-270-7092. 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.
/CHASE P. HINCKLEY/Examiner, Art Unit 2124