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 Amendments
Claims 1-20 are currently pending in this case and have been examined and
addressed below. This communication is a Final Rejection in response to the
Amendment to the Claims and Remarks filed on 01/02/2026.
Claims 1, 3-4, 11, 13-14, are amended claims.
Claims 2, 5-10, 12, and 15-20 are original claims.
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 a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-20 are drawn to a method and a system, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites method for decomposing an initial question to generate a plurality of sub-question; applying a first sub-question of the plurality of sub-questions and an image to generate an answer and a confidence score; determining that the confidence score is below a threshold value; and applying a second sub-question of the plurality of sub-questions, responsive to the determination that the confidence score is below a threshold value, to generate a final answer.
Independent claim 11 recites a system for decompose an initial question to generate a plurality of sub-questions; apply a first sub-question of the plurality of sub-questions and an image to a visual question answering model to generate an answer and a confidence score; determine that the confidence score is below a threshold value; and apply a second sub-question of the plurality of sub-questions to the visual question answering model, responsive to the determination that the confidence score is below a threshold value, to generate a final answer.
These steps amount to certain methods of organizing human activity which includes functions relating to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This 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 recite the additional elements of computer, hardware processor, and a memory.
These elements are recited at a high-level of generality such that it amounts to mere instructions to apply the exception because this is an example of applying the abstract idea by use of general-purpose computer which does not integrate the abstract idea into a practical application.
Claims 1 and 11 recite a visual answer questioning model. This limitation recites a mathematical algorithm that amounts to mere instructions to apply the exception (MPEP 2106.05(f)(2)).
The above claims, as a whole, are therefore directed to an abstract idea.
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.
As discussed above with respect to integration of the abstract idea into a
practical application, the claims recite the additional elements of computer, hardware processor, and a memory.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1 and 11 recite a visual answer questioning model. This limitation recites a mathematical algorithm that amounts to mere instructions to apply the exception (MPEP 2106.05(f)(2)).
For the reasons stated, these claims fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claims 2 and 12 recite applying the initial question to a decomposition model to generate a perception question relating to the initial question.
Dependent claims 3 and 13 recite generates a new confidence score for the final answer.
Dependent claims 5 and 15 recite performing an action responsive to the final answer.
Dependent claims 6 and 16 recite wherein the image is an image of a patient and the final answer relates to diagnosis of a medical condition of the patient.
Dependent claims 8 and 18 recite wherein the action includes assistance to medical decision making by healthcare personnel.
Dependent claims 9 and 19 recite selecting the threshold value based on a domain of the initial question and the image.
Each of these steps of the preceding dependent claims 2-3,5-6, 8-9, 12-13, 15-16, and 18-19 only serve to further limit or specify the features of independent claims 1 or 11 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim.
Dependent claims 4 and 14 recite iteratively applying sub-questions of the plurality of sub-questions to the visual question answering model until the new confidence score exceeds the threshold value. Dependent claims 10 and 20 recite wherein the visual question model is a machine learning model. These limitations recite a visual question answering model which amounts to a mathematical algorithm that amounts to mere instructions to apply the exception (MPEP 2106.05(f)(2)).
Dependent claims 7 and 17 recite wherein the action includes automatic administration of a treatment to the patient on the basis of the diagnosis. This step amounts to insignificant extra solution activity. When determining if a particular treatment and prophylaxis as a practical application under Step 2A Prong Two, Examiner considered the factors presented in the MPEP 2106.04(d)(2).
Factor A: The Particularity Or Generality Of The Treatment Or Prophylaxis. The administration of a treatment from the abstract idea is not "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). The administration of a treatment is never specifically stated and the particular disease or condition that the administered treatment is treating is never specifically stated. Therefore, the claims recite a high-level recitation of a treatment plan without explicitly providing a particular treatment for a particular disease or medical condition.
Factor B. Whether the Limitation(s) Have More Than a Nominal or Insignificant
Relationship to the Exception. The treatment limitation does not have a
significant relationship to the judicial exception – that is it does not integrate the
law of nature into a practical application. As stated above, because the administered treatment and the particular disease or condition is never explicitly stated, any possible treatment could not reasonably be considered known in the art as a treatment for any disease.
Factor C. Whether the Limitation(s) Are Merely Extra-Solution Activity or A Field
of Use. The treatment or prophylaxis limitation does not impose meaningful limits
on the judicial exception and is only extra-solution activity or a field-of-use (see
MPEP § 2106.05(g))). Automatic administration of a treatment to the patient on the basis of the diagnosis is well known, well-understood, routine, and conventional. This position is supported by (1) Filippidis et al, Cerebral venous sinus thrombosis: review of the demographics, pathophysiology, current diagnosis, and treatment (2009), teaching on the prompt treatment of patients with Cerebral venous sinus thrombosis (CVST) should follow 4 directions to provide a complete treatment plan and maximize the chance of favorable outcome. Every effort should be made to identify and appropriately manage the predisposing and precipitating factors, administer antithrombotic therapy, lower and normalize the elevated ICP, and provide symptomatic treatment for seizures, headache, and visual disturbances (Pg.6 Treatment); (2) Sharp et al, Screening for Depression Across the Lifespan: A Review of Measure for Use in Primary Care Settings (2002), teaching on early identification and proper treatment significantly decrease the negative impact of depression in most patients and most patients with depression can be effectively treated with pharmacotherapeutic and psychotherapeutic modalities (Pg. 1); and (3) Duffy et al, Chapter One - Tissue and Blood Biomarkers in Lung Cancer: A Review (2018), teaching on for those diagnosed with locally advanced disease not suitable for surgery, radiotherapy in combination with platinum-based chemotherapy may be used (Pg. 1 Introduction). Therefore, automatic administration of a treatment to the patient on the basis of the diagnosis does not impose meaningful limits on the judicial exception and is not sufficient to amount to significantly more than the recited judicial exception.
Therefore, this limitation recites the prophylactic step as a tool which only serves as insignificant post solution activity (MPEP § 2106.05(g) - insignificant pre/post-solution activity) and is therefore not a practical application of the recited judicial exception.
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.
Claim(s) 1-4, 9-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Costabello (US 20200356829 A1) in view of Datla (US 20190377796 A1) in view of Murdock (US 11880661 B2) in view of Brown (US 20140258286 A1).
REGARDING CLAIM 1
Costabello teaches a computer-implemented method for visual question answering, comprising:
applying a first sub-question of the plurality of sub-questions and an image to a visual question answering model to generate an answer and a confidence score; ([Para. 0018] FIG. 1 illustrates a first example of a system 100 for multi-modal visual query. The system 100 may receive an input query and an input image. [Para. 0019] The system may generate a response the input image and input query. The response may include a structured or unstructured answer to the query. [Para. 0055] After each of the multi-modal embeddings have been identified, the multi-modal scoring controller 504 may score the validity of each statement based on distance on the results set (i.e. confidence score)120.)
Costabello do not explicitly teach, however Datla teaches
decomposing an initial question to generate a plurality of sub-question; ([Para. 0013] (i) receiving, via the user interface, the query from the user; (ii) decomposing the received query into one or more identified sub-questions.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello and incorporate open-domain real-time question answering as taught by Datla, with the motivation of providing answers to open-domain questions in real-time and provide information aligned with the questioner's focus, emotion, or subjectivity (Datla Para. 0002 and 0005.)
Costabello/ Datla do not explicitly teach, however Murdock teaches
determining that the confidence score is below a threshold value; ([Col. 3, Lines 3-5] Responsive to determining the answer has the confidence assessment below the confidence threshold)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, with the motivation of causes the question answering system to approximate the target answering frequency (Murdock Col. 2, Lines 13-14).
Costabello/ Datla/ Murdock do not explicitly teach, however Brown teaches
and applying a second sub-question of the plurality of sub-questions to the visual question answering model, responsive to the determination that the confidence score is below a threshold value, to generate a final answer. ([Para. 0056] This model is now applied, and candidate answers are ranked according to classification score with the classification score used as a measure of answer confidence, that is, possible candidate answers are compared and evaluated by applying the prediction function to the complete feature set or subset thereof. [Para. 0111] It should also be noted that if the system is unable to find an answer or to find an answer with a high score (based, e.g., upon comparison to a preset threshold), the system might ask user a clarifying question. [Para. 0110] Referring back to FIG. 3, during the final phase of processing 60, all of the candidate answer features are aggregated and merged, and the final candidate answer scoring function is applied (as described above with respect to the example scores provided in Table 1. Since a given candidate answer may appear in multiple passages, the Final Merge/Rank annotator must collect results across CASes, normalize and merge candidate answers, merge feature scores produced by the same answer scorer across multiple instances of the candidate answer, and aggregate the results. The normalized, merged, and aggregated results are input to the scoring function to produce a final score for the candidate answer. The final scoring results are saved as an answer and//or delivered to a user: It should be noted that Final merging and ranking is incremental, i.e., the machine provides the best so far answer as the computation on different nodes completes. Once all nodes complete, the final(top) answer(s) is delivered.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, and incorporate providing answers to questions based on any corpus of data implements as taught by Brown, with the motivation of providing a computing infrastructure and methodology for conducting questions and answers (Brown Para. 0023).
REGARDING CLAIM 2
Costabello/ Datla/ Murdock/ Brown teach the method of claim 1, Datla further teaches wherein decomposing the initial question includes applying the initial question to a decomposition model to generate a perception question relating to the initial question. ([Para. 0013] (i) receiving, via the user interface, the query from the user; (ii) decomposing the received query into one or more identified sub-questions; (viii) extracting from the selected question-context-answer triple for each identified sub-question using a trained model, a portion of the associated information comprising an answer to the identified sub-question; (ix) generating, from the extracted portions, a natural language answer comprising a response to the query posed by the user, wherein the generated response is subject to one or more user-defined answer priorities; and (x) providing the response to the user via the user interface. [Para. 0045] For each sub-question, a generated question-context-answer triple comprising information most likely to comprise an answer to the identified sub-question.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate open-domain real-time question answering as taught by Datla, with the motivation of providing answers to open-domain questions in real-time and provide information aligned with the questioner's focus, emotion, or subjectivity (Datla Para. 0002 and 0005.)
REGARDING CLAIM 3
Costabello/ Datla/ Murdock/ Brown teach the method of claim 1, Murdock further teaches wherein applying the second sub-question to the visual question answering model further generates a new confidence score for the final answer. ([Col. 1, Lines 26-47] Receiving, by a question answering system having a confidence threshold, plural questions from one or more user devices. The method includes processing, by the question answering system, each respective one of the plural questions by: generating an answer to the respective one of the plural questions; determining a confidence score of the answer; in response to determining the confidence score is greater than the confidence threshold, increasing the confidence threshold and returning the answer to a respective one of the one or more user devices that generated the respective one of the plural questions; and in response to determining the confidence score is less than the confidence threshold, decreasing the confidence threshold and not returning the answer to the respective one of the one or more user devices that generated the respective one of the plural questions. The increasing the confidence threshold and the decreasing the confidence threshold are performed such that the question answering system returns answers for the plural questions at a rate that approximates the pre-defined target answering frequency by converging to a dynamic equilibrium frequency equal to that target frequency.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, with the motivation of causes the question answering system to approximate the target answering frequency (Murdock Col. 2, Lines 13-14).
REGARDING CLAIM 4
Costabello/ Datla/ Murdock/ Brown teach the method of claim 3, Murdock teaches further comprising iteratively applying sub-questions of the plurality of sub-questions to the visual question answering model until the new confidence score exceeds the threshold value. ([Col. 2 Lines 64-67 and Col.3 Lines 1-3] selecting a confidence threshold for answering a question; processing iteratively a plurality of questions from a user by the system to determine for each question an answer with a confidence assessment; responsive to determining the answer has the confidence assessment above the confidence threshold returning the answer and incrementing the confidence threshold)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, with the motivation of causes the question answering system to approximate the target answering frequency (Murdock Col. 2, Lines 13-14).
REGARDING CLAIM 9
Costabello/ Datla/ Murdock/ Brown teach the method of claim 1, Murdock further teaches further comprising selecting the threshold value based on a domain of the initial question and the image. ([Col. 2, Lines 49-53] the QA system automatically and dynamically adjusts the confidence threshold to approximate a target answering frequency, which is a specified percentage of the time for which the system provides an answer to a question. [Col. 4, Lines 4-12] QA system accesses a body of knowledge about the domain, or subject matter area (e.g., financial domain, medical domain, legal domain, etc.) where the body of knowledge (knowledgebase) can be organized in a variety of configurations, such as but not limited to a structured repository of domain-specific information, such as ontologies, or unstructured data related to the domain, or a collection of natural language documents about the domain. [Col. 6, Lines 10-15] For a question answering system with a given level of quality, the accuracy and the median confidence for answers will often vary widely from domain to domain and can change dramatically within a domain when updating the content and/or systematically altering the kinds of questions being asked)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, with the motivation of causes the question answering system to approximate the target answering frequency (Murdock Col. 2, Lines 13-14).
REGARDING CLAIM 10
Costabello/ Datla/ Murdock/ Brown teach the method of claim 1, Costabello further teaches wherein the visual question model is a machine learning model. ([Para. 0016] The multi-modal embeddings may be included in a multi-modal embedding model and trained via supervised learning.)
REGARDING CLAIM 11
Costabello teaches a system for visual question answering, comprising:
a hardware processor; ([Para. 0078] a processor 816 or multiple processors. [Para. 0079] Examples of the processor 816 may include a general processor, a central processing unit, logical CPUs/arrays, a microcontroller, a server. )
and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to: ([Para. 0080] The logic may include computer executable instructions or computer code stored in the memory 820 or in other memory that when executed by the processor 816. [Para. 0081] The memory 820 may include non-volatile and/or volatile memory, such as a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), or flash memory. )
apply a first sub-question of the plurality of sub-questions and an image to a visual question answering model to generate an answer and a confidence score; ([Para. 0018] FIG. 1 illustrates a first example of a system 100 for multi-modal visual query. The system 100 may receive an input query and an input image. [Para. 0019] The system may generate a response the input image and input query. The response may include a structured or unstructured answer to the query. [Para. 0055] After each of the multi-modal embeddings have been identified, the multi-modal scoring controller 504 may score the validity of each statement based on distance on the results set (i.e. confidence score)120.)
Costabello does not explicitly teach, however Datla teaches
decompose an initial question to generate a plurality of sub-questions; ([Para. 0013] (i) receiving, via the user interface, the query from the user; (ii) decomposing the received query into one or more identified sub-questions.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello and incorporate open-domain real-time question answering as taught by Datla, with the motivation of providing answers to open-domain questions in real-time and provide information aligned with the questioner's focus, emotion, or subjectivity (Datla Para. 0002 and 0005.)
Costabello/ Datla do not explicitly teach, however Murdock teaches
determine that the confidence score is below a threshold value; ([Col. 3, Lines 3-5] Responsive to determining the answer has the confidence assessment below the confidence threshold)
Costabello/ Datla/ Murdock do not explicitly teach, however Brown teaches
and apply a second sub-question of the plurality of sub-questions to the visual question answering model, responsive to the determination that the confidence score is below a threshold value, to generate a final answer. ([Para. 0056] This model is now applied, and candidate answers are ranked according to classification score with the classification score used as a measure of answer confidence, that is, possible candidate answers are compared and evaluated by applying the prediction function to the complete feature set or subset thereof. [Para. 0111] It should also be noted that if the system is unable to find an answer or to find an answer with a high score (based, e.g., upon comparison to a preset threshold), the system might ask user a clarifying question. [Para. 0110] Referring back to FIG. 3, during the final phase of processing 60, all of the candidate answer features are aggregated and merged, and the final candidate answer scoring function is applied (as described above with respect to the example scores provided in Table 1. Since a given candidate answer may appear in multiple passages, the Final Merge/Rank annotator must collect results across CASes, normalize and merge candidate answers, merge feature scores produced by the same answer scorer across multiple instances of the candidate answer, and aggregate the results. The normalized, merged, and aggregated results are input to the scoring function to produce a final score for the candidate answer. The final scoring results are saved as an answer and//or delivered to a user: It should be noted that Final merging and ranking is incremental, i.e., the machine provides the best so far answer as the computation on different nodes completes. Once all nodes complete, the final(top) answer(s) is delivered.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, and incorporate providing answers to questions based on any corpus of data implements as taught by Brown, with the motivation of providing a computing infrastructure and methodology for conducting questions and answers (Brown Para. 0023).
REGARDING CLAIM 12
Claim(s) 12 is/are analogous to Claim(s) 2, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
REGARDING CLAIM 13
Claim(s) 13 is/are analogous to Claim(s) 3, thus Claim(s) 13 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
REGARDING CLAIM 14
Claim(s) 14 is/are analogous to Claim(s) 4, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
REGARDING CLAIM 19
Claim(s) 19 is/are analogous to Claim(s) 9, thus Claim(s) 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 9.
REGARDING CLAIM 20
Claim(s) 20 is/are analogous to Claim(s) 10, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 10.
Claim(s) 5-6 and 15- 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello (US 20200356829 A1) in view of Datla (US 20190377796 A1) in view of Murdock (US 11880661 B2) in view of Brown (US 20140258286 A1) in view of Morris (US 20210335491 A1).
REGARDING CLAIM 5
Costabello/ Datla/ Murdock/ Brown teach the method of claim 1, however Morris teaches further comprising performing an action responsive to the final answer. ([Para. 0017] The diagnostic and treatment system also provides multiple diagnosis and treatment options such that a provider can use their experience, judgment, and physical examination of the patient to choose a diagnosis and a treatment for the diagnosis. The diagnosis and treatment system may also track patient compliance with the treatment plan and update its knowledge base based on patient treatment outcomes. [Para. 0044] The treatment model 126 generally receives as input a selected diagnosis from the set of differential diagnoses and generates treatment options based on the selected diagnosis. The treatment model 126 may be implemented using various algorithms, machine learning models, or combinations of both. For example, the treatment model 126 may use an algorithm to provide set treatment options for a particular diagnosis. The treatment model 126 may use clustering to select treatment options with a higher likelihood of success for a particular condition based on, for example, treatment data received from a provider or patient indicating whether a treatment was successful.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate diagnosis and treatment system to obtain diagnostic input as taught by Morris, with the motivation of providing accurate patient diagnosis (Morris Para. 0003).
REGARDING CLAIM 6
Costabello/ Datla/ Murdock/ Brown/ Morris teach the method of claim 5, Morris further teaches wherein the image is an image of a patient and the final answer relates to diagnosis of a medical condition of the patient. ([Para. 0022] The patient may use the patient device 104 to take a picture of the affected areas and the diagnostic and treatment system may further use the image and/or other diagnostic input alongside or in place of diagnostic questions to generate differential diagnoses. [Para 0065] The system 102 may output a single diagnosis or may output the subset of differential diagnoses with indicators of likelihood, rankings, or other information indicating a relative strength of the diagnosis.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, providing answers to questions based on any corpus of data implements as taught by Brown, and incorporate diagnosis and treatment system to obtain diagnostic input as taught by Morris, with the motivation of providing accurate patient diagnosis (Morris Para. 0003).
REGARDING CLAIM 15
Claim(s) 15 is/are analogous to Claim(s) 5, thus Claim(s) 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
REGARDING CLAIM 16
Claim(s) 16 is/are analogous to Claim(s) 6, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 6.
Claim(s) 7-8 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Costabello (US 20200356829 A1) in view of Datla (US 20190377796 A1) in view of Murdock (US 11880661 B2) in view of Brown (US 20140258286 A1) in view of Morris (US 20210335491 A1) in view of Miranda (US 20180137433 A1).
REGARDING CLAIM 7
Costabello/ Datla/ Murdock/ Brown/ Morris teach the method of claim 6, however Miranda teaches wherein the action includes automatic administration of a treatment to the patient on the basis of the diagnosis. ([Para. 0069] Once the computing device 102 and/or the question-answer (QA) processing device 110 determines a diagnosis for the patient, the computing device 102 and/or the QA processing device 110 can provide a recommending course of treatment based on the capabilities of the health care provider. As an example, if the course of treatment involves administering a specific medicine, the computing device 102 and/or the QA processing device 110 can determine if the health care provider has the medicine in stock, and if the medicine is available, the computing device 102 and/or the QA processing device 110 can notify a health care professional to retrieve the medicine and administer the medicine to the user.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, providing answers to questions based on any corpus of data implements as taught by Brown, the diagnosis and treatment system to obtain diagnostic input as taught by Morris, and incorporate the self-training engine of a question and answer system as taught by Miranda, with the motivation of providing an improved data processing apparatus to assist with diagnosing and treating patients (Miranda Para. 0001 and 0018).
REGARDING CLAIM 8
Costabello/ Datla/ Murdock/ Brown/ Morris teach the method of claim 6, however Miranda teaches wherein the action includes assistance to medical decision making by healthcare personnel. ([Para. 0065] For healthcare related questions, natural language, hypothesis generation, and evidence-based learning capabilities of a plurality of QA processing devices can be used to contribute to clinical decision support systems for use by health care professionals. To aid the health care professionals in treatment of patients, once a health care professional has posed a query to the QA processing device describing symptoms and other related factors, the QA processing device can parse the input to identify important pieces of information.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of multi-modal visual question answering system as taught by Costabello, open-domain real-time question answering as taught by Datla, and incorporate unsupervised dynamic confidence thresholding for answering questions as taught by Murdock, providing answers to questions based on any corpus of data implements as taught by Brown, the diagnosis and treatment system to obtain diagnostic input as taught by Morris, and incorporate the self-training engine of a question and answer system as taught by Miranda, with the motivation of providing an improved data processing apparatus to assist with diagnosing and treating patients (Miranda Para. 0001 and 0018).
REGARDING CLAIM 17
Claim(s) 17 is/are analogous to Claim(s) 7, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
REGARDING CLAIM 18
Claim(s) 18 is/are analogous to Claim(s) 8, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 8.
Response to Arguments
Applicant's arguments, see pgs. 6-7 “Rejections under 35 U.S.C.101” filed 01/02/2026 have been fully considered but they are not persuasive.
Applicant asserts that the claims integrate any abstract idea with a practical application. In particular, the claims reflect an improvement to a technology by improving the efficiency of a question-answering model.
Examiner respectfully disagrees. An improvement to the abstract idea improving the efficiency of a question-answering model does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG,921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to improving the efficiency of the question-answering model by decomposing questions. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Applicant's arguments, see pgs. 7-9 “Rejections under 35 U.S.C. 103” filed 01/02/2026 have been fully considered are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Brown, as per the rejection above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patricia K Edouard whose telephone number is (571)272-6084. The examiner can normally be reached Monday - Friday 7:30 AM - 5:00 PM.
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/P.K.E./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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