DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a final rejection
Claims 1-3, 6-17, 20-25 are pending
Claims 4-5, 18-19 were cancelled
Claims 1, 6-7, 10-13, 15-16, 20-25 were amended
Claims 1-3, 6-17, 20-25 are rejected under 35 USC § 101
Claims 1-3, 6-17, 20-25 are rejected under 35 USC § 102
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 11-29-2021
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7-13-2024, 9-11-2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-3, 6-17, 20-25 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-3, 6-17, 20-25 the claims recite an abstract idea of “disease prediction”.
Independent Claims 1, 13 and 15 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 13, 15 recite a system, user equipment and method respectively regarding “disease prediction”.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
receive a set of input queries from one or more users .., said set of input queries pertaining to one or more symptoms associated with a disease;
extract a first set of attributes from the received set of input queries, wherein the first set of attributes pertain to the one or more symptoms of the disease;
wherein the first set of attributes further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state,
wherein the dialogue state comprises a conversation,
wherein the utterance state comprises one or more exchanges that occur,
wherein the patient state comprises a complete information of the user, a plurality of responses to the set of input queries,
wherein the disease state comprises a complete information about a target disease and
wherein the symptom state comprises information about a plurality of symptoms associated with the target disease.
extract a second set of attributes from the received set of input queries, the second set of attributes pertaining to demographic conditions and location;
based on the extracted first and second attributes, generate, .. a ..model configured to process each input query;
based on the .. model, map, .. each said input query with a respective response, wherein said respective response is associated with an information service, and wherein the information service is further associated with a knowledge graph comprising interconnections between a target disease and the one or more symptoms associated with the disease of the one or more users, ..
map, by using the ..model, the one or more symptoms with a set of parameters, wherein the set of parameters are determined by searching a medical knowledgebase for similarity of the set of parameters with the one or more symptoms, wherein the medical knowledgebase comprises static medical resources, .. and peer reviewed journals for a target disease; and,
predict, the disease based on the mapped one or more symptoms with the set of parameters associated with the target disease; and
based on a scoring mechanism that comprises any or a combination of differential diagnosis of the Patient State, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes.
facilitate a final response corresponding to the predicted disease to the one or more users.
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “disease prediction”. Alternatively it belongs to certain methods of organizing nonhuman animal activity under managing personal behavior or relationships or interactions between animals as it recites “disease prediction”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 1, 13 and 15 recite:
one or more processors operatively coupled to a plurality of first computing devices the one or more processors coupled with a memory, wherein said memory (204) stores instructions which when executed by the one or more processors (202) causes said system to
plurality of first computing devices;
generate, by using an artificial intelligence (Al) engine, a trained mode configured to process each input queryl;
the Al engine is further configured to: map, by using the trained model;
the medical knowledgebase comprises, medical databases, online medical resources
Claim 13 recites:
A user equipment (UE);
a receiver;
receive, by the receiver;
extract by the processor;
wherein the Al engine is associated with the processor;
a processor operably coupled to the memory;
Claim 15 recites:
Receiving/Extracting/ facilitating, by the one or more processors;
wherein the Al engine is associated with the processor;
predicting, by the Al engine.
Amount to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claims 1, 13 and 15 recite:
one or more processors operatively coupled to a plurality of first computing devices the one or more processors coupled with a memory, wherein said memory (204) stores instructions which when executed by the one or more processors (202) causes said system to
plurality of first computing devices;
generate, by using an artificial intelligence (Al) engine, a trained mode configured to process each input queryl;
the Al engine is further configured to: map, by using the trained model;
the medical knowledgebase comprises, medical databases, online medical resources
Claim 13 recites:
A user equipment (UE);
a receiver;
receive, by the receiver;
extract by the processor;
wherein the Al engine is associated with the processor;
a processor operably coupled to the memory;
Claim 15 recites:
Receiving/Extracting/ facilitating, by the one or more processors;
wherein the Al engine is associated with the processor;
predicting, by the Al engine.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “disease prediction”: claims 2-3, 6-12, 14, 16-17, 20-25
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims 2, 7, 9-12, 14, 16, 20-25 amount to no more than mere instructions to apply the exception using a generic computer, or merely using a computer as a tool to implement the abstract idea as even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims 2, 7, 9-12, 14, 16, 20-25 amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the additional elements alone, and in combination do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6-17, 20-25 are rejected by 35 U.S.C. 102(a)(1) as being anticipated by Cao et.al. (US 20170344711 A1) hereinafter “Cao”
Regarding claims 1, 13 and 15 Cao teaches:
A system for facilitating a response of prediction (response prediction) of a disease (medical conditions), said system comprising; (See at least [0045] via: "...response prediction module 145 can determine one or more medical conditions, each having a confidence value, from results returned from querying the expert knowledge graph model 140. ... response prediction module 145 can select medical conditions returned from expert knowledge graph model 140 having a weighting or confidence factor greater than a predetermined threshold value..”)
one or more processors (at least one hardware processor) operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory (coupled to a memory), wherein said memory stores instructions which when executed by the one or more processors (memory comprising instructions that, ... executed by the at least one hardware processor) , causes said system to: (See at least [0021] via: "...executable instructions stored on non-transitory computer-readable medium... a system can comprise at least one hardware processor coupled to a memory comprising instructions that, when executed by the at least one hardware processor...”)
receive (receiving) a set of input queries from one or more users (a user medical query ) associated with the plurality of first computing devices, said set of input queries pertaining to one or more symptoms (one or more symptoms) associated with a disease; (See at least [0015] via: "...method for automating medical diagnosis can received from an electronic device of a user, a user medical query comprising one or more symptoms."; claim 1, "receiving from an electronic device of a user, a user medical query comprising one or more symptoms...")
extract a first set of attributes (extracts one or more keywords) from the received set of input queries (from the search query), wherein the first set of attributes pertain to the one or more symptoms (representing symptoms of a medical condition) of the disease; (See at least [0025] via: "...In response to the search query, search engine 122 extracts one or more keywords from the search query, the keywords representing symptoms of a medical condition as described by a user..)
wherein the first set of attributes further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state,
wherein the dialogue state comprises a conversation between the user and the system, (See at least [0022] via: "... FIG. 1A is a block diagram illustrating a networked online medical diagnosis system 100 according to one embodiment of the invention. Referring to FIG. 1, system 100 includes, but is not limited to, one or more client devices 101-102 communicatively coupled to server 104 over network 103. Client devices 101-102, also referred to as user devices, may be any type of client devices such as a personal computer (e.g., desktops, laptops, and tablets), a “thin” client, a personal digital assistant (PDA), a Web enabled appliance, a Smartwatch, or a mobile phone (e.g., Smartphone), etc. Network 103 may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination thereof, wired or wireless. Client devices 101 and 102 can have one or more applications 111, such as a web browser, to facilitate interaction with server 104...”; in addition see at least [0033] via: "... the first user can connect and communicate with the online medical diagnosis system 120 based on the user information of the first user in the user profile 125 in a more friendly and efficient manner...”
wherein the utterance state comprises one or more exchanges that occur between the user and the system, (See at least [0070] via: "...IO devices 607 may include an audio device. An audio device may include a speaker and/or a microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and/or telephony functions..”)
wherein the patient state comprises a complete information of the user, a plurality of responses to the set of input queries, (See at least [0043] via: "...the user profile 125 that relates to the query. For example, a 60 year old male with a high weight to height ratio, having a history of high blood pressure, may correlate higher to adult onset type II diabetes than a 5 year child with a normal weight to height ratio and no history of diabetes...”; in addition see at least [0054] via: "... A user profile can further indicate whether the user is a smoker 410 or lives in a smoking environment, such as a child of a parent who smokes. A user profile can further include one or more medications 404 and an indication 412 for which the medication is prescribed. A user profile 400 for an older person can include medical history for conditions such as heart disease 405, lung disease 314, chest pain 406, joint pain 414, back pain 407 and headaches 415. A user profile for an adult may indicate an occupation 408 of the user...”)
wherein the disease state comprises a complete information about a target disease (See at least [0045] via: "...response prediction module 145 can determine one or more medical conditions, each having a confidence value, from results returned from querying the expert knowledge graph model 140. ... response prediction module 145 can select medical conditions returned from expert knowledge graph model 140 having a weighting or confidence factor greater than a predetermined threshold value..”) and
wherein the symptom state comprises information about a plurality of symptoms associated with the target disease. (See at least [0016] via: "...The correlation graph model can be dynamically constructed from user-entered queries and reflects the weighted relations among arbitrary medical related words, phrases, and sentences and entities in the expert knowledge graph model. For example, "my baby has high body temperature" will be mapped to "fever" with a score of 0.9, "cold" with a score of 0.6, "cough" with a score of 0.5, etc. Data sources used to build the correlation graph model include the combined stored query logs of all users and crawled data from online medical resources such as medical articles, research papers, and other online medical information...")
extract a second set of attributes (extracts one or more keywords) from the received set of input queries (from the search query), the second set of attributes pertaining to demographic conditions and location (lives in a smoking environment); (See at least [0025] via: "...In response to the search query, search engine 122 extracts one or more keywords from the search query..”; in addition see at least [0054] via: "... A user profile can further indicate whether the user is a smoker 410 or lives in a smoking environment, such as a child of a parent who smokes. ... A user profile for an adult may indicate an occupation 408 of the user )
based on the extracted first and second attributes, generate, by using an artificial intelligence (Al) engine , a trained model (training a correlation graph model) configured to process each input query; (See at least [0011] via: "... FIG. 5 is a block flow diagram of a method of training a correlation graph model according to one embodiment of the invention “; in addition see at least [0031] via: "...Server 104 may include a correlation graph model (CGM) learning system 150 that can dynamically update a correlation graph model 130. A correlation graph model 130 correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions. ... The CGM learning system 150 can use a learning model 175 such as Baysian, naive Bayes, linear regression, random neural network, recurrent neural network, or long short term memory to determine correlation weights of keywords 160 to expert keywords 170 for the correlation graph model 130. ..”).
based on the trained model, map, by using the Al engine, each said input query with a respective response, wherein said respective response is associated with an information service (medical articles, research papers, and other medical resources 107 ), and wherein the information service is further associated with a knowledge graph (expert knowledge graph) comprising interconnections between a target disease and the one or more symptoms (correlation graph model 130 correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions) associated with the disease of the one or more users, (See at least [0011] via: "...FIG. 5 is a block flow diagram of a method of training a correlation graph model according to one embodiment of the invention..."; in addition see at least [0019] via: "...One or more of the keywords can be filtered by limiting the one or more keywords to only those keywords that meet a threshold correlation to an expert keyword. In an embodiment, in response to determining that more than one medical condition is determined using the expert knowledge graph model, the expert knowledge graph model can be used to determine one or more additional symptoms that will reduce the number of medical conditions determined by the expert knowledge graph model. A user can be prompted to answer whether the user has any of the one or more additional symptoms..."; in addition see at least [0031] via: "... Server 104 may include a correlation graph model (CGM) learning system 150 that can dynamically update a correlation graph model 130. A correlation graph model 130 correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions. The CGM learning system 150 can parse a history or database of query logs stored from a large number of user query sessions with the online diagnosis system 120 to generate a database of keywords ...a medical resource crawler 165 can use keywords 160 and expert keywords 170 to crawl medical articles, research papers, and other medical resources 107 to determine a correlation of keywords 160 obtained from user queries to expert keywords 170 obtained from expert knowledge graph model 140...”; in addition see at least [0035] via: "...The correlated keywords 160 to expert keywords 170 represent one or more entities of the expert knowledge graph model 140..."; in addition see at least [claim 1] via: "...correlating the one or more keywords to one or more expert keywords using a correlation graph model; determining one or more medical conditions based upon the one or more expert keywords using an expert knowledge graph model...")
wherein the Al engine (214) is further configured to:
Map ("my baby has high body temperature" will be mapped to "fever" with a score of 0.9), by using the trained model (correlation graph model), the one or more symptoms with a set of parameters (correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions), wherein the set of parameters are determined by searching a medical knowledgebase for similarity of the set of parameters with the one or more symptoms, wherein the medical knowledgebase comprises static medical resources, medical databases, online medical resources, and peer reviewed journals (medical articles, research papers, and other medical resources 107) for a target disease; (See at least [0016] via: "...The correlation graph model can be dynamically constructed from user-entered queries and reflects the weighted relations among arbitrary medical related words, phrases, and sentences and entities in the expert knowledge graph model. For example, "my baby has high body temperature" will be mapped to "fever" with a score of 0.9, "cold" with a score of 0.6, "cough" with a score of 0.5, etc. Data sources used to build the correlation graph model include the combined stored query logs of all users and crawled data from online medical resources such as medical articles, research papers, and other online medical information..."; in addition see at least [0024] via: "...Online medical diagnosis system 120 can include search engine 122, correlation graph model 130, entity selection module 135, expert knowledge graph model 140, response predication module 145, and user profiles 125..."; in addition see at least [0031] via: "... Server 104 may include a correlation graph model (CGM) learning system 150 that can dynamically update a correlation graph model 130. A correlation graph model 130 correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions. The CGM learning system 150 can parse a history or database of query logs stored from a large number of user query sessions with the online diagnosis system 120 to generate a database of keywords 160. In an embodiment, a medical resource crawler 165 can use keywords 160 and expert keywords 170 to crawl medical articles, research papers, and other medical resources 107 to determine a correlation of keywords 160 obtained from user queries to expert keywords 170 obtained from expert knowledge graph model 140. ..."; in addition see at least [0044] via: "...The search activity information may be captured and recorded in query log database 152 when a search was performed. The server 104 can retrieve user information of the first user from query log database 152 and/or from user profile 125. The server 104 optionally can transmit at least some of the user information of the first user from the query log database 152 to the online diagnosis system 120, which may be compiled into a user profile...") and,
predict, the disease (Response predication module 145 can determine ... symptoms that may narrow the ... medical conditions or increase, .. the confidence level of a medical condition) based on the mapped one or more symptoms with the set of parameters associated with the target disease; (See at least [0025] via: "Response predication module 145 can determine one or more symptoms that may narrow the one or more medical conditions or increase, or reduce, the confidence level of a medical condition. Response prediction module 145 can prompt the user at the search engine interface 122 to provide additional symptoms..."; in addition see at least [0045] via: "...response prediction module 145 can determine one or more medical conditions, each having a confidence value, from results returned from querying the expert knowledge graph model 140. In an embodiment, response prediction module 145 can select medical conditions returned from expert knowledge graph model 140 having a weighting or confidence factor greater than a predetermined threshold value..."; in addition see at least [0046] via: "...Medical conditions can be filtered by adjusting the confidence level of a medical condition based upon the user profile 125. In an embodiment, operation 350 can be used in conjunction with operation 330 to filter entities and medical conditions using the user profile"); and
based on a scoring mechanism that comprises any or a combination of differential diagnosis of the Patient State, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes. (See at least [0016] via: "...The correlation graph model can be dynamically constructed from user-entered queries and reflects the weighted relations among arbitrary medical related words, phrases, and sentences and entities in the expert knowledge graph model. For example, “my baby has high body temperature” will be mapped to “fever” with a score of 0.9, “cold” with a score of 0.6, “cough” with a score of 0.5, etc. Data sources used to build the correlation graph model include the combined stored query logs of all users and crawled data from online medical resources such as medical articles, research papers, and other online medical information...”; in addition see at least [0045] via: "...response prediction module 145 can determine one or more medical conditions, each having a confidence value, from results returned from querying the expert knowledge graph model 140. In an embodiment, response prediction module 145 can select medical conditions returned from expert knowledge graph model 140 having a weighting or confidence factor greater than a predetermined threshold value..”)
facilitate a final response (determining ... medical conditions ... and transmitting a response) corresponding to the predicted disease to the one or more users. (See at least [0035] via: "...a user can log in to the online medical diagnosis system 120 and a user profile 125 can be looked up using the user login. The user's query can be parsed into one or more keywords 160. The keywords 160 can be looked up in the correlation graph model 130 to determine a weighted correlation of keywords 160 of the user query to expert keywords 170 obtained from the expert knowledge graph model ......... Response prediction module 145 can determine that expert knowledge graph model 140 has returned one or more medical conditions..."; in addition see at least [claim 1] via: "...determining one or more medical conditions based upon the one or more expert keywords using an expert knowledge graph model; and transmitting a response to the electronic device based on the one or more medical conditions...")
Regarding claims 2, 16 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 15 respectively. Cao also teaches:
wherein said respective response is mapped with the information service and is transmitted in real-time to the plurality of first computing devices. (See at least [0016] via: "... The correlation graph model can be dynamically constructed from user-entered queries and reflects the weighted relations among arbitrary medical related words, phrases, and sentences and entities in the expert knowledge graph model. For example, “my baby has high body temperature” will be mapped to “fever” with a score of 0.9, “cold” with a score of 0.6, “cough” with a score of 0.5, etc. Data sources used to build the correlation graph model include the combined stored query logs of all users and crawled data from online medical resources such as medical articles, research papers, and other online medical information..”; in addition see at least [claim 1] via: "...A computer-implemented method for automating medical diagnosis, the method comprising: receiving from an electronic device of a user, a user medical query comprising one or more symptoms; parsing the user medical query into one or more keywords representing the symptoms; correlating the one or more keywords to one or more expert keywords using a correlation graph model; determining one or more medical conditions based upon the one or more expert keywords using an expert knowledge graph model; and transmitting a response to the electronic device based on the one or more medical conditions..”)
Regarding claims 3, 17 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 15 respectively. Cao also teaches:
wherein the knowledge graph design is configured to model one or more dependencies associated with one or more conditional queries to be received from the one or more users. (See at least [0035] via: "... A client, such as client 101, can begin using the online medical diagnosis system 120 by issuing a medical query to query/user interface of search engine 122. ...The user's query can be parsed into one or more keywords 160. The keywords 160 can be looked up in the correlation graph model 130 to determine a weighted correlation of keywords 160 of the user query to expert keywords 170 obtained from the expert knowledge graph model 140. ... Entity selection model 135 can select a subset of the entities for further diagnosis. In an embodiment, the weighting factors of keywords 160 to expert keywords 170 can be modified by referring to user profile 125. For example, gender, age, previous medical conditions, and combinations of these can be used to modify the weight of keyword 160 correlation to expert keywords 170. Entity selection module 135 can select entities for analysis by expert knowledge graph model 140 based upon an entity having a minimum threshold correlation value of a keyword 160 to an expert keyword 170. For example, using the user profile 125, a smoker of age 60 may correlate the term “cough” more heavily to “emphysema” or “lung disease” than a 5 year old that does not live in a smoke-affected environment. Similarly, a 50 year old female may correlate a breast soreness or lump more heavily to breast cancer than would a 6 year old male. After adjustment of correlation weights of keywords 160 to expert keywords 170 using the user profile 125, entity selection module 135 can pass selected entities meeting a threshold correlation weight to the expert knowledge graph model 140. Expert knowledge graph model 140 can determine one or more medical conditions from the expert keywords 170 in the entities selected by entity selection module 135 with a confidence factor for each medical condition. Response prediction module 145 can determine that expert knowledge graph model 140 has returned one or more medical conditions. ... If expert knowledge graph model 140 returns more than one medical condition, response prediction module 145 can prompt the user to provide more symptoms that can reduce the number of results returned from response prediction module 145....”) The Examiner interprets that the association of symptom to target disease is conditional depending on the profile of the user...”)
Regarding claims 6, 20 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 15 respectively. Cao also teaches:
wherein the Al engine is further configured to predict the disease based on a plurality of contexts such as any or a combination of disease priors, risk factors, lab-tests, body organs, body systems. (See at least [0017] via: "...The expert knowledge graph model is composed of real doctors' knowledge. For example, “cold” can cause “fever” with 70% probability, “stuff nose” with 90% probability, and “cough” with 50% probability. By using a graph model to represent the expert knowledge, the decision model is easily scalable and can easily integrate new doctor knowledge. The expert knowledge graph model uses the results from the correlation graph model to predict a response to a user's query with high accuracy and quality...”; in addition see at least [0048] via: "... If, in operation 355, more than one medical condition has been identified and the medical conditions are not sufficiently narrowed, then in operation 360 the response prediction module 145 can prompt the user to provide more information that will help identify a particular medical condition. In an embodiment, the user may be prompted to answer specific questions about particular symptoms that may related to the one or more medical conditions. In an embodiment, a user may be prompted to answer questions about medical history that may be related to the one or more medical conditions. In such case, the method 300 resumes at operation 315...”; in addition see at least [0054] via: "... A user profile can further indicate whether the user is a smoker 410 or lives in a smoking environment, such as a child of a parent who smokes. A user profile can further include one or more medications 404 and an indication 412 for which the medication is prescribed. A user profile 400 for an older person can include medical history for conditions such as heart disease 405, lung disease 314, chest pain 406, joint pain 414, back pain 407 and headaches 415. A user profile for an adult may indicate an occupation 408 of the user...”)
Regarding claims 7, 21 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 15 respectively. Cao also teaches:
wherein the Al engine is configured to provide an automatic learning of one or more weights and the set of parameters from the knowledge graph and a feedback analysis to self-improve prediction of the disease; (See at least [0016] via: "... The correlation graph model can be dynamically constructed from user-entered queries and reflects the weighted relations among arbitrary medical related words, phrases, and sentences and entities in the expert knowledge graph model. For example, “my baby has high body temperature” will be mapped to “fever” with a score of 0.9, “cold” with a score of 0.6, “cough” with a score of 0.5, etc. Data sources used to build the correlation graph model include the combined stored query logs of all users and crawled data from online medical resources such as medical articles, research papers, and other online medical information. ..”and
update the knowledge graph based on a revised one or more weights in the knowledge graph as suggested by a plurality of medical experts. (See at least [0031] via: "... Server 104 may include a correlation graph model (CGM) learning system 150 that can dynamically update a correlation graph model 130. A correlation graph model 130 correlates user search terms representing medical symptoms with expert keywords of an expert knowledge graph model 140 related to medical conditions. The CGM learning system 150 can parse a history or database of query logs stored from a large number of user query sessions with the online diagnosis system 120 to generate a database of keywords 160. In an embodiment, a medical resource crawler 165 can use keywords 160 and expert keywords 170 to crawl medical articles, research papers, and other medical resources 107 to determine a correlation of keywords 160 obtained from user queries to expert keywords 170 obtained from expert knowledge graph model 140. .”; in addition see at least [0061] via: "... In operation 520, learning model 175 can be used to update the correlation graph model 130 with new keywords 160 and weights correlating keywords 160 to expert keywords 170. The CGM learning system 150 can perform its dynamic learning asynchronously from the user queries of the online medical diagnosis system 120...”)
Regarding claims 8, 22 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 15 respectively. Cao also teaches:
wherein the system is further configured to collect a plurality of points of data chosen by a plurality of medical experts to gather, refine and enrich the medical knowledgebase. (See at least [0017] via: "... The expert knowledge graph model is composed of real doctors' knowledge.. The expert knowledge graph model is relatively static and can be updated manually by medical professionals, such as doctors, as needed...”; in addition see at least [0059] via: "... In operation 510, medical resource crawler 165 can use keywords 160 to crawl medical articles, research papers, and other medical resources 107 to determine a correlation of keywords 160 obtained from user queries to expert keywords 170 obtained from expert knowledge graph model 140...”)
Regarding claims 9, 14 Cao teaches the invention as claimed and detailed above with respect to claims 1 and 13 respectively. Cao also teaches:
wherein an interface unit associated with the plurality of first computing devices is configured to display a plurality of information associated with the predicted disease. (See at least [0066] via: "...System 600 may further include a graphics interface that communicates with optional graphics subsystem 604, which may include a display controller, a graphics processor, and/or a display device...”; in addition see at least [0025] via: " ... Response predication module 145 can determine one or more symptoms that may narrow the one or more medical conditions or increase, or reduce, the confidence level of a medical condition. Response prediction module 145 can prompt the user at the search engine interface 122 to provide additional symptoms. In an embodiment, response prediction module 145 can request that the user answer specific symptom questions. The above process can be repeated using the additional information provided by the user until a single medical condition can be identified or the number of medical conditions is sufficiently narrowed...”)
Regarding claims 10, 23 Cao teaches the invention as claimed and detailed above with respect to claims 1&4 and 15&18 respectively. Cao also teaches: wherein the Al engine is further configured to:
Obtain any or a combination of a previous utterance and a response by the user for the previous utterance question for retrieval of information of a user from the dialogue state, wherein the previous utterance question is derived by the system from the received set of input queries; (See at least [0025] via: ".. response prediction module 145 can request that the user answer specific symptom questions. The above process can be repeated using the additional information provided by the user until a single medical condition can be identified or the number of medical conditions is sufficiently narrowed..”; in addition see at least [0048] via: “.. If, in operation 355, more than one medical condition has been identified and the medical conditions are not sufficiently narrowed, then in operation 360 the response prediction module 145 can prompt the user to provide more information that will help identify a particular medical condition. In an embodiment, the user may be prompted to answer specific questions about particular symptoms that may related to the one or more medical conditions. In an embodiment, a user may be prompted to answer questions about medical history that may be related to the one or more medical conditions...”)
obtain one or more user answered symptoms from the symptom state; (See at least [0019] via: ".. A user can be prompted to answer whether the user has any of the one or more additional symptoms...”)
obtain a demography of the user from the patient state; (See at least [0054] via: "... A user profile can further indicate whether the user is a smoker 410 or lives in a smoking environment, such as a child of a parent who smokes. ... A user profile for an adult may indicate an occupation 408 of the user..” ) and
obtain, from the disease state, a list of topmost diseases that relate with the one or more symptoms of the user with highest probability. (See at least [0047] via: "..In operation 355, it can be determined whether one medical condition has been identified by the response prediction module 145, or the medical conditions identified have been sufficiently narrowed. Response prediction module 145 may have identified a plurality of medical conditions that are closely related or have confidence factors that are too close to determine a specific one medical condition, but are sufficiently narrowed to determine a recommendation to a user..”)
Regarding claims 11, 24 Cao teaches the invention as claimed and detailed above with respect to claims 1&4&10 and 15&18&23 respectively. Cao also teaches:
wherein the Al engine is further configured to: understand the previous utterance to decide the next utterance based on any or a combination of a previous demographic utterance response, a previous symptom utterance response, and a previous attribute response. (See at least [0025] via: ".. response prediction module 145 can request that the user answer specific symptom questions. The above process can be repeated using the additional information provided by the user until a single medical condition can be identified or the number of medical conditions is sufficiently narrowed..”; in addition see at least [0048] via: “.. If, in operation 355, more than one medical condition has been identified and the medical conditions are not sufficiently narrowed, then in operation 360 the response prediction module 145 can prompt the user to provide more information that will help identify a particular medical condition. In an embodiment, the user may be prompted to answer specific questions about particular symptoms that may related to the one or more medical conditions. In an embodiment, a user may be prompted to answer questions about medical history that may be related to the one or more medical conditions...”’ in addition see at least [0044] via: "..In operation 335, entity selection module 135 can select entities for analysis by the expert knowledge graph model 140 from the inferred entities list 132. Entity selection module 135 can select entities having a correlation weight of keyword 160 to expert keyword 170 above a predetermined threshold value. In operation 340, expert knowledge graph model 140 can use the expert keywords 170 and correlation weights of keywords 160 to expert keywords 170 to query the expert knowledge graph model 140 ...”)
Regarding claims 12, 25 Cao teaches the invention as claimed and detailed above with respect to claims 1&4&10 and 15 respectively. Cao also teaches:
wherein the Al engine is further configured to: Filter, unrelated one or more symptoms from the knowledge graph; (See at least [0019] via: "... One or more medical conditions can be determined based upon the one or more expert keywords using an expert knowledge graph model. The determination of one or more medical conditions can each have a confidence level that is distinct from the correlation weight of keywords to expert keywords. In an embodiment, the correlation graph model is dynamically updated, and the expert knowledge graph model is static. One or more of the keywords can be filtered by limiting the one or more keywords to only those keywords that meet a threshold correlation to an expert keyword.,,”...)
determine one or more highly likely diseases based on the one or more symptoms of the user, the scores associated with the one or more symptom associated with the disease; (See at least [0019] via: ".. in response to determining that more than one medical condition is determined using the expert knowledge graph model, the expert knowledge graph model can be used to determine one or more additional symptoms that will reduce the number of medical conditions determined by the expert knowledge graph model. A user can be prompted to answer whether the user has any of the one or more additional symptoms...”; in addition see at least [0045] via: "...In operation 345, response prediction module 145 can determine one or more medical conditions, each having a confidence value, from results returned from querying the expert knowledge graph model 140. In an embodiment, response prediction module 145 can select medical conditions returned from expert knowledge graph model 140 having a weighting or confidence factor greater than a predetermined threshold value...) . and,
predict, an actual disease based on the filtering of unrelated one or more symptoms and determination the one or more highly likely diseases. (See at least [0046] via: ".. In operation 350, medical conditions identified by response prediction module 145 can optionally be filtered using the user profile 125. Medical conditions can be filtered by adjusting the confidence level of a medical condition based upon the user profile 125. In an embodiment, operation 350 can be used in conjunction with operation 330 to filter entities and medical conditions using the user profile 125. In an embodiment, one or operation 330 or 350 can be used to filter entities or medical conditions, respectively...”; in addition see at least [0047] via: "..In operation 355, it can be determined whether one medical condition has been identified by the response prediction module 145, or the medical conditions identified have been sufficiently narrowed. Response prediction module 145 may have identified a plurality of medical conditions that are closely related or have confidence factors that are too close to determine a specific one medical condition, but are sufficiently narrowed to determine a recommendation to a user..”)
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
WHANGBO (KR 102246827 B1) – A Symptom Recognition Method Of Diseases For Senior User Chatbot Based On Language Model - teaches: a method for recognizing symptoms of frequent diseases based on a language model for a chatbot used for a senior user, implemented by: the chatbot analyzing a text type query transmitted from a user terminal or a voice uttered by a user to output a result for a corresponding request to a user; and a service providing server connected to the chatbot via a network and receiving request information including query or voice information analyzed by the chatbot to transmit process information for the request information via a deep learning based classification model to the chatbot. The method comprises: a first step of allowing the chatbot to receive the query transmitted by the user terminal or the voice uttered by the user; a second step of converting the information received by the chatbot into the request information, resulted from an analytical process via the language model, and transmitting the request information to the service providing server; a third step of classifying the request information, received by the service providing server, via a deep learning based classification model to generate process information with respect to the request information and to transmit the process information to the chatbot; and a fourth step of allowing the chatbot to output the process information to the user. Therefore, the present invention can estimate disease symptoms via the utterance of a senior user and further provide the information on related diseases to the user. In addition, the present invention can estimate the symptoms of frequent diseases of the senior user from the utterance of the user, provide information of doubtful diseases to the senior user via a combination of the classified symptoms, and assist health care.
Response to Arguments
Applicant's arguments filed 8-14-2026, have been fully considered but not found
persuasive.
Applicant amended independent claims 1, 13, 15 and dependent claims 6-7, 10-12, 16, 20-25 as posted in the above analysis with additions underlined and deletions as .
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 13 and 15 since the claims recite a system, user equipment and method respectively regarding “disease prediction”.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “disease prediction” or alternatively as belonging to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “disease prediction”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that the invention does not recite an abstract idea since the claim with limitations cannot does not recite a mental process since it cannot practically be performed in the human mind or alternatively it does not recite method of organizing human activity. Accordingly, amended claims 1, 13 & 15 are not directed to an abstract idea.
The Applicant argues that independent claims 1, 13, 15 rather recite a specific computer implemented system that utilizes one or more processors, an artificial intelligence (AI)
engine, a trained machine-learning model, a medical knowledgebase, and a knowledge graph to process user-generated medical queries and predict a disease, whereby what is required includes extraction of multiple specialized attributes including a dialogue state, utterance state, patient state, disease state, and symptom state from user interactions. The Applicant further argues that such multidimensional state extraction and machine-learning processing cannot practically be performed in the human mind and instead requires specialized computing operations. Accordingly, amended independent claims 1, 13, 15 are directed to a technological process implemented through particular computing components rather than an abstract idea.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Examiner explains the method to select the abstract idea, which is to strip the additional elements from the claims.
Regarding Claim 1:
As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of the amended limitations of claim 1
one or more processors operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes said system to:
receive a set of input queries from one or more users associated with the plurality of first computing devices, said set of input queries pertaining to one or more symptoms associated with a disease;
extract a first set of attributes from the received set of input queries, wherein the first set of attributes pertain to the one or more symptoms of the disease;
wherein the first set of attributes further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state, wherein the dialogue state comprises a conversation between the user and the system, wherein the utterance state comprises one or more exchanges that occur between the user and the system, wherein the patient state comprises complete information of the user and a plurality of responses to the set of input queries, wherein the disease state comprises complete information about a target disease, and wherein the symptom state comprises information about a plurality of symptoms associated with the target disease;
extract a second set of attributes from the received set of input queries, the second set of attributes pertaining to demographic conditions and location;
based on the extracted first and second attributes, generate, by using an artificial intelligence (Al) engine, a trained model configured to process each input query;
based on the trained model, map, by using the Al engine, each said input query with a respective response, wherein said respective response is associated with an information service, and wherein the information service is further associated with a knowledge graph comprising interconnections between a target disease and the one or more symptoms associated with the disease of the one or more users, wherein the Al engine is further configured to:
map, by using the trained model, the one or more symptoms with a set of parameters, wherein the set of parameters are determined by searching a medical knowledgebase for similarity of the set of parameters with the one or more symptoms, wherein the medical knowledgebase comprises static medical resources, medical databases, online medical resources, and peer reviewed journals for a target disease; and
predict, the disease based on the mapped one or more symptoms with the set of parameters associated with the target disease; and
based on a scoring mechanism that comprises any or a combination of differential diagnosis of the patient state, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases, and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes; and
facilitate a final response corresponding to the predicted disease to the one or more users.
Regarding Claim 13:
As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of the amended limitations of claim 13
one or more processors and a receiver operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes said UE to:
receive, by the receiver, a set of input queries from one or more users associated with the plurality of first computing devices, said set of input queries pertaining to one or more symptoms associated with a disease;
extract, by the one or more processors, a first set of attributes from the received set of input queries, wherein the first set of attributes pertain to the one or more symptoms of the disease;
wherein the first set of attributes further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state, wherein the dialogue state comprises a conversation between the user and the UE, wherein the utterance state comprises one or more exchanges that occur between the user and the UE, wherein the patient state comprises complete information of the user and a plurality of responses to the set of input queries, wherein the disease state comprises complete information about a target disease, and wherein the symptom state comprises information about a plurality of symptoms associated with the target disease;
extract, by the one or more processors, a second set of attributes from the received set of input queries, the second set of attributes pertaining to demographic conditions and location;
based on the extracted first and second attributes, generate, by using an artificial intelligence (Al) engine, a trained model configured to process each input query, wherein the Al engine is associated with the one or more processors;
based on the trained model, map, by using the Al engine, each said input query with a respective response, wherein said respective response is associated with an information service, and wherein the information service is further associated with a knowledge graph comprising interconnections between a target disease and the one or more symptoms associated with the disease of the one or more users, wherein the AI engine is further configured to:
map, by using the trained model, the one or more symptoms with a set of parameters, wherein the set of parameters are determined by searching a medical knowledgebase for similarity of the set of parameters with the one or more symptoms, wherein the medical knowledgebase comprises static medical resources, medical databases, online medical resources, and peer reviewed journals for a target disease; and,
predict, the disease based on the mapped one or more symptoms with the set of parameters associated with the target disease; and
based on a scoring mechanism that comprises any or a combination of differential diagnosis of the patient state, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases, and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes, and
facilitate, a final response corresponding to the predicted disease to the one or more users.
Regarding Claim 15
As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of the amended limitations of claim 15
receiving, by the one or more processors, a set of input queries from one or more users associated with the plurality of first computing devices, said set of input queries pertaining to one or more symptoms associated with a disease, wherein the one or more processors are operatively coupled to a plurality of first computing devices, the one or more processors coupled with a memory, wherein said memory stores instructions that are executed by the one or more processors;
extracting, by the one or more processors, a first set of attributes from the received set of input queries, wherein the first set of attributes pertain to the one or more symptoms of the disease;
wherein the first set of attributes further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state, wherein the dialogue state comprises a conversation between the user and the one or more processors, wherein the utterance state comprises one or more exchanges that occur between the user and the method, wherein the patient state comprises complete information of the user and a plurality of responses to the set of input queries, wherein the disease state comprises complete information about a target disease, and wherein the symptom state comprises information about a plurality of symptoms associated with the target disease;
extracting, by the one or more processors, a second set of attributes from the received set of input queries, the second set of attributes pertaining to demographic conditions and location;
based on the extracted first and second attributes, generating, by using an artificial intelligence (Al) engine, a trained model configured to process each input query, wherein the Al engine is associated with the one or more processors;
based on the trained model, mapping, by using the Al engine, each said input query with a respective response, wherein said respective response is associated with an information service, and wherein the information service is further associated with a knowledge graph comprising interconnections between a target disease and the one or more symptoms associated with the disease of the one or more users, wherein the method further comprises the steps of:
mapping, by using the trained model by the Al engine, the one or more symptoms with a set of parameters, wherein the set of parameters are determined by searching a medical knowledgebase for similarity of the set of parameters with the one or more symptoms, wherein the medical knowledgebase comprises static medical resources, medical databases, online medical resources, and peer reviewed journals for a target disease; and,
predicting, by the Al engine, the disease based on the mapped one or more symptoms with the set of parameters associated with the target disease; and
based on a scoring mechanism that comprises any or a combination of differential diagnosis of the patient state, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases, and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes and
facilitating, by the one or more processors, a final response corresponding to the predicted disease to the one or more users
The selected abstract idea (boldened limitations) of claim 1, 13, 15 belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “disease prediction”. Alternatively, the claim belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people (including social activities, and following rules or instructions) as it recites: “disease prediction” (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
Regarding Step 2A Prong Two.
The Applicant argues that even if claims 1, 13 and 15 were to recite an abstract idea they do recite additional elements that integrate the judicial exception into a practical application. Applicant submits that the additional elements recited in the claims do provide technical improvements that demonstrate integration of the alleged abstract idea into a practical application, and that the Applicant's Specification does describe the technical improvements provided by the claims.
Specifically, amended independent claims 1, 13, 15 recite a particular technological architecture including first computing devices, processors, memory, an AI engine, a medical knowledge base, and a knowledge graph that collectively perform disease prediction. Amended independent claim 1 is not directed merely to collecting information and displaying results; instead, it specifies how information is
processed through state extraction, machine-learning model generation, symptom to parameter mapping, and disease prediction operations. Furthermore, the amended independent claims 1, 13, 15 improve the functioning of computerized disease prediction systems by incorporating multiple state representations, knowledge-graph
relationships, and a specialized scoring mechanism that includes differential diagnosis,
symptom coverage score, Relative Disease Score, and Attribute Mismatch Decay score.
These features enable the system to generate more accurate and context-aware disease
predictions than conventional systems that merely process symptom information. Thus, the claims provides a technological improvement in the field of AI-assisted medical diagnosis. Accordingly, amended independent claim 1 applies any alleged abstract concept in a meaningful way that imposes a real-world technological solution to a medical information processing problem. Amended independent claims 1, 13, 15 therefore integrate any alleged judicial exception into a practical application
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Applicant is interpreting the practical application of the invention colloquially. Instead, it should be analyzed as to whether the additional elements amount to more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. As a result, the Examiner restates that claims 1, 13 and 15 do not integrate the abstract idea into a practical application.
Neither claims 1, 13 and 15 recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites:
a plurality of aggregators configured to communicate in a meshed network
each of the plurality of aggregators comprising a processor and a transceiver configured to communication with one or more sensors, each of the plurality of aggregators being configured to determine signal information associated with at least one signal of the communication with the one or more sensors of an asset
the one or more sensors, each of the one or more sensors associated with the asset
the one or more sensors configured to emit the at least one signal to communicate, with the plurality of aggregators when in range of the plurality of aggregators within the geo-fence in the designated area
a central computer in communication with the plurality of aggregators and configured to receive the information from at least one of the plurality of aggregators
the central computer configured to determine, from at least one of the plurality of aggregators, a plurality of parameters;
Claim 7 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to an asset to a central computer;
determining a localization, by the central computer;
using an algorithm on the central computer;
proximity score is calculated using one or more signals from the one or more tags and one or more additional tags as captured by each aggregator.
Claim 15 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to one or more assets to a central computer;
determining, by a central computer, a duration of time the asset remained in the designated area;
determining, by a central computer, a proximity score;
determining, by
determining by the central computer, a contact status.
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical application the additional elements should be shown to impose a meaningful limit on the abstract idea. A colloquial interpretation of a practical application is not enough.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
Regarding Step 2B
The Applicant argues that even if the invention fails to recite a practical application, the amended independent claims 1, 13, 15 recite significantly more than any alleged abstract idea because it includes an inventive combination of technological features that are neither conventional nor generic. Specifically the claimed combination is not merely the use of a generic computer as a tool. Instead, the processors execute a specifically recited sequence of operations that transforms raw user queries into disease predictions through machine-learning techniques, knowledge graph analysis, medical knowledgebase searching, and specialized scoring calculations. Such an ordered combination of elements is not shown to be well-understood, routine, or conventional. Additionally, amended independent claim 1 provides technological improvements in the field of computerized disease prediction by enabling more accurate prediction through the integration of patient-specific, disease-specific, demographic, and location-based information into a unified AI framework.
The Examiner disagrees since the Applicant’s arguments are not persuasive. Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 102.
The Applicant argues that Cao fails to teach the amended limitation of claims 1, 7 and 15. The Applicant argues that the amended claims recite "based on the extracted first and second attributes, generate, by using an artificial intelligence (AI) engine, a trained model configured to process each input query", where the first set of attributes pertains to symptoms and the second set of attributes pertains to "demographic conditions and location". Cao instead recites a method of training the correlation graph model that involves parsing query logs into keywords, using a medical resource crawler to crawl medical resources, and using a learning model to determine correlation weights of keywords to expert keywords. However, Cao does not generate the trained model based on extracted demographic conditions and location attributes as required by amended independent claims 1, 13, 15. While Cao uses demographic information from user profiles to modify correlation weights during query processing, this is distinct from generating the trained model itself based on extracted demographic and location attributes. Amended independent claims 1, 13, 15 requires that the trained model generation step incorporate both the symptom attributes and the demographic/location attributes. Furthermore the amended claims recite that the first set of attributes "further comprises a dialogue state, an utterance state, a patient state, a disease state, and a symptom state" with specific definitions for each state. Cao does not disclose extracting attributes comprising these five distinct states with the specific definitions recited. In addition the amended claims are not taught by any of the prior art used to reject the invention. In addition the claims recite predicting the disease "based on a scoring mechanism that comprises any or a combination of differential diagnosis of the patient state, a symptom coverage score associated with a coverage of the one or more symptoms expressed by the user corresponding to the disease, a Relative Disease Score pertaining to relevance of the disease compared with other diseases, and an Attribute Mismatch Decay score pertaining to a mismatch of the first set of attributes with the second set of attributes". Instead Cao's response prediction module determines medical conditions with confidence values from querying the expert knowledge graph model and can select medical conditions having a weighting or confidence factor greater than a predetermined threshold value. Cao does not disclose the specific scoring mechanism with the specifically named scores (symptom coverage score, Relative Disease Score, and Attribute Mismatch Decay score) as recited by amended independent claims 1, 13, 15. Therefore, in view of the above, Cao fails to anticipate the above-mentioned features as recited in amended independent claims 1, 13, 15.
The examiner disagrees since the Applicant’s arguments are not persuasive. The Applicant is mainly focused on the method of obtaining results related to predicting disease whereby the claims recite an artificial intelligence methodology while Cao recites a method of training the correlation graph model that involves parsing query logs into keywords, using a medical resource crawler to crawl medical resources, and using a learning model to determine correlation weights of keywords to expert keywords.
Although the claimed invention is based on a method using AI, the prior art recited by Cao uses a crawler with a different algorithm which nevertheless produces identically the same output. Hence the prior art recites a “literal anticipation” of the recited independent claims 1, 13, 15. Hence it is appropriate to use the prior art recited by Cao, since under 35 U.S.C. § 102, a prior art reference can anticipate a claim if it discloses all elements of the claim in a single reference, even if the method is different, provided the disclosure is enabling and the claim is fully covered as is the case with Cao.
If the claim recites a specific method and the prior art shows a different method achieving the same result, the prior art may still be relevant if it renders the claimed method obvious to a person skilled in the art. For novelty, the prior art must disclose all elements of the claim, either literally or in a way that makes the claim obvious. If the prior art’s method is different, it may not destroy novelty unless the claim is broader than the method recited. MPEP § 706 states that the examiner must cite the best references and explain the pertinence of each If the prior art’s method is different but the claim is broader than the method, the examiner can still reject for lack of novelty if the prior art discloses the claimed invention in a different form or method as is the case with Cao’s prior art.
Hence based on BRI of the amended claims, Cao according to the Examiner does teach independent claims 1, 13, 15 in addition to all its dependent claims. Accordingly the Examiner maintains the 35 USC §102 rejection based on prior art.
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-3, 6-17, 20-25 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101, in addition to maintaining the rejection of claims 1-3, 6-17, 20-25 under 35 USC §102 based on prior art. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
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 PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
