DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
Notice to Applicant
In the amendment dated 06/08/2026, the following has occurred: claims 1, 9, and 17 have been amended; claims 2-8 and 10-16 remain unchanged; and no new claims have been added.
Claims 1-17 are pending.
Effective Filing Date: 11/24/2021
Response to Arguments
35 U.S.C. 101 Rejections:
Step 2A, Prong One:
Applicant argues that the amended claims are not directed to an abstract idea. Applicant argues in view of McRO and states that the present claims are similar in that they improve the functioning of a computer or other technology. Applicant further states that the claim limitations cannot be practically performed within the human mind. Examiner however respectfully disagrees. First, the abstract idea is not directed towards mental processes, rather, the abstract idea can be explained under certain methods of organizing human activity and mathematical concepts.
Applicant also argues that the claims now recite a specific, technology-oriented architecture comprising a dialogue state module, symptom state module, and disease state module that collectively manage and update system states during an ongoing interaction. The modules reflect usage of models where data is inputted and outputted from them. The updating of the module though is broad though. Is this more than inputting data into a model?
Applicant further state that the claims define a machine-driven, stateful processing framework that governs the flow of data and decision-making within the system, thereby moving amended independent claim 1 outside the realm of generalized human activity. Applicant further argues that there is a specialized sequence of data processing operations including conditional determination of symptomatic queries, extraction of contextual attributes, batch-wise identification and sorting of candidate attributes, and mapping to diseases using a medical knowledge database and knowledge graph. These claim steps supposedly cannot practically be performed mentally or manually, nor do the claims include mathematical concepts. Examiner however respectfully disagrees as the claims recite both manual steps and mathematical concepts. The steps do recite a sequence but that sequence is not limited to a computer processing sequence.
Step 2A, Prong Two:
Applicant states that the steps of extracted contextual attributes are processed through batch-wise candidate identification, sorting, and mapping to diseases using a medical knowledge database and knowledge graph, followed by filtering based on user-specific parameters such as age and gender are operations which result in context-aware and dynamically prioritized symptom sets which are then used to generate adaptive queries. Examiner however respectfully disagrees that this is inherently a process limited to a particular technical field. The steps above can also recite an abstract concept as presented by Examiner below in the updated 35 U.S.C. 101 rejection section.
Applicant further states that there is a conversion of data structures which enables and improves human-computer interaction. Examiner however would respectfully like to point to the specification for a lack of discussion on this point. Furthermore, the claims do recite a natural language generation layer which is broad and does not necessarily have to be a computer step of natural language processing (this is why it is part of the abstract idea). The formats and conversions between are not clearly presented in the claims.
Step 2B:
Applicant argues with respect to BASCOM and states that the present claims are similar. The claims of BASCOM recite user-specific web filters that are non-conventionally and non-routinely located at the ISP server rather than a user’s device. Applicant states that the present claims operate together to transform raw user inputs into prioritized medical queries, which is not a routine or generic use of a computer. The present claims however are not similar to those of BASCOM because the claims lack a non-routine/non-conventional arrangement in a similar manner. The claims as whole recite an abstract idea with additional elements which do not add significantly more than the abstract idea.
Applicant further states that there is a non-conventional and non-generic combination of elements. Applicant further states that there are specific technical operations which help generate more accurate and context-sensitive queries, thereby improving overall system performance. Initially, this is not discussed in the specification. Furthermore, the claims do not recite such additional elements which integrate with the abstract idea to form significantly more.
Applicant further states that there is a specialized machine for adaptive medical query generation and that the claim elements produce a synergistic technical effect. As stated above however, the specification lacks this discussion and the claims do not reflect this as they only recite additional elements which apply the abstract idea.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-8 are drawn to a system, claims 9-16 are drawn to a method, and claim 17 is drawn to a device, each of which is within the four statutory categories. Claims 1-17 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a system for generating adaptive medical queries to determine disease symptoms, the system comprising:
a) a processor;
b) a memory coupled to the processor, wherein the memory comprises processor-executable instructions, which on execution, cause the processor to:
1) orchestrate, via c) a dialogue state module, a conversation between a user and the system by maintaining a state of the conversation including questions asked by the system and answers given by the user;
2) represent, via d) a symptom state module, a symptom-state in terms of presence or absence of symptoms and attributes;
3) determine, via e) a disease state module, a probability of whether a disease is present or not present based on given symptoms;
4) receive one or more inputs from the user, in response to one or more queries corresponding to one or more symptoms associated with the disease of the user;
5) update the symptom state module based on the one or more inputs from the user, and output from the symptom state module to update the disease state module;
6) determine, if a previous query from the one or more queries is a symptomatic query, and determine, if each of the one or more inputs from the user is an affirmative input;
7) extract a set of contextual attributes corresponding to a medical context, from the one or more inputs received from the user, when the one or more queries is the symptomatic query, and each of the one or more inputs from the user is the affirmative input;
8) identify batch-wise candidate attributes from the extracted set of contextual attributes, and sort the batch-wise candidate attributes corresponding to each of the one or more symptoms, in a display order;
9) map the one or more symptoms in the sorted batch-wise candidate attributes to the disease, by searching a medical knowledge database;
10) calculate a disease-symptom weighed score for the mapped one or more symptoms to the disease, by retrieving a symptom bucket corresponding to the disease in a knowledge graph;
11) filter the one or more symptoms based on an age and a gender of the user, and sort the one or more symptoms based on the calculated disease-symptom weighed score and an inter-dependency on other attributes in the batch-wise candidate attributes of each symptom;
12) transmit the filtered and sorted one or more symptoms to a natural language generation layer for converting the one or more symptoms, attribute canonical names, and unique Identities (IDs) to a human-understandable form; and
13) generate subsequently, one or more adaptive medical queries in the human-understandable form, by predicting the one or more adaptive medical queries based on the converted one or more symptoms, the attribute canonical names, and the unique Identities (IDs), to determine the disease of the user.
Claim 1 recites, in part, performing the steps of 1) orchestrate a conversation between a user and the system by maintaining a state of the conversation including questions asked by the system and answers given by the user, 2) represent a symptom-state in terms of presence or absence of symptoms and attributes, 3) determine a probability of whether a disease is present or not present based on given symptoms, 4) receive one or more inputs from the user, in response to one or more queries corresponding to one or more symptoms associated with the disease of the user, 5) update something based on the one or more inputs from the user, and output from something to update something, 6) determine, if a previous query from the one or more queries is a symptomatic query, and determine, if each of the one or more inputs from the user is an affirmative input, 7) extract a set of contextual attributes corresponding to a medical context, from the one or more inputs received from the user, when the one or more queries is the symptomatic query, and each of the one or more inputs from the user is the affirmative input, 8) identify batch-wise candidate attributes from the extracted set of contextual attributes, and sort the batch-wise candidate attributes corresponding to each of the one or more symptoms, in a display order, 9) map the one or more symptoms in the sorted batch-wise candidate attributes to the disease, by searching a medical knowledge database, 10) calculate a disease-symptom weighed score for the mapped one or more symptoms to the disease, by retrieving a symptom bucket corresponding to the disease in a knowledge graph, 11) filter the one or more symptoms based on an age and a gender of the user, and sort the one or more symptoms based on the calculated disease-symptom weighed score and an inter-dependency on other attributes in the batch-wise candidate attributes of each symptom, 12) transmit the filtered and sorted one or more symptoms to a natural language generation layer for converting the one or more symptoms, attribute canonical names, and unique Identities (IDs) to a human-understandable form and 13) generate subsequently, one or more adaptive medical queries in the human-understandable form, by predicting the one or more adaptive medical queries based on the converted one or more symptoms, the attribute canonical names, and the unique Identities (IDs), to determine the disease of the user. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes how one could assess a user’s input to determine another query to ask a user.
Claim 1 also recites, in part, performing the steps of 3) determine a probability of whether a disease is present or not present based on given symptoms, 9) map the one or more symptoms in the sorted batch-wise candidate attributes to the disease, by searching a medical knowledge database, and 10) calculate a disease-symptom weighed score for the mapped one or more symptoms to the disease, by retrieving a symptom bucket corresponding to the disease in a knowledge graph. These steps correspond to Mathematical Concepts.
Going forward, the above abstract concepts will be considered as a single abstract idea. Independent claims 9 and 17 recite similar limitations and are also directed to an abstract idea under the same analysis.
Depending claims 2-8 and 10-16 include all of the limitations of claims 1 and 9, and therefore likewise incorporate the above described abstract idea. Depending claims 2 and 10 add the additional steps of “receive one or more symptoms of the disease as an input to one or more queries which are reasoning-based queries, from the user”, “determine top ‘n’ disease scores with a highest probability, based on the received one or more symptoms of the disease, and the age and the gender of the user”, “determine top ‘k’ disease scores, by calculating a heuristic score using the received one or more symptoms”, “determine candidate symptoms of the determine top ‘k’ disease scores, and compute candidate symptom scores based on the symptom bucket, and pre-defined disease”, “analyze, if the top ‘n’ disease scores are greater or equal to the top ‘k’ disease scores with respect to the computed candidate symptom scores”, “map the candidate symptoms to the disease, by searching the medical knowledge database, when the top ‘n’ disease scores are greater or equal to the top ‘k’ disease scores”, “calculate the disease-symptom weighed score for the mapped candidate symptoms to the disease, by retrieving a symptom bucket corresponding to the disease in a knowledge graph”, “filter the one or more symptoms based on the age and the gender of the user, and sort the one or more symptoms based on the calculated disease-symptom weighed score and pre-defined deceased scores”, and “transmit the filtered and sorted candidate symptoms to the natural language generation layer for converting the candidate symptoms to the human-understandable form”; claims 3 and 16 add the additional steps of “iterate over all diseases, to sum the disease-symptom weighted score for each disease”, “determine a probability score of each disease, for the summed-up disease-symptom weighted score”, “determine the sum of the probability score of all the diseases”, “calculate a normalized disease score from the probability score of each disease and the sum of the probability score of all the diseases”, “calculate a symptom score of each disease, based on the disease-symptom weightage score and the normalized disease score”, and “update the symptom score for each disease, and update a final symptom score, by summing the symptom score of each disease”; and claims 5 and 15 add the additional steps of “determine at least one of an insurance policy, an insurance premium, a health index, and a preventive measures guided by one or more health authorities, upon receiving a response from the user for the one or more adaptive medical queries” and “generate the determined at least one of the insurance policy, the insurance premium, the health index, and the preventive measures guided by one or more health authorities”. Additionally, the limitations of depending claims 4, 6-8 and 11-14 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-8 and 10-16 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 9 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a user equipment comprising a) a processor, b) a memory coupled to the processor, wherein the memory comprises processor-executable instructions, c) a dialogue state module, d) a symptom state module, and e) a disease state module to perform the claimed steps.
The a) processor, b) memory, c) dialogue state module, d) symptom state module, and e) disease state module in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0053] where there is a general-purpose computer, see MPEP 2106.05(f)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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 and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) a processor, b) a memory, c) a dialogue state module, d) a symptom state module, and e) a disease state module to perform the claimed steps amounts to no more than mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(f) recites that the following limitations are not significantly more:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates queries utilizing a) a processor, b) a memory, c) a dialogue state module, d) a symptom state module, and e) a disease state module, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-17 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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