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 .
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1 recites (additional elements crossed out):
An
generating medical examination data to which unique IDs have been assigned, respectively, wherein the medical examination data comprise user's personal information and symptom and/or disease-related data of the user;
obtaining language selection information that is information of a first language, selected among preset languages,
obtaining answer data for the medical examination data corresponding to the language selection information from the user terminal; and
classifying all of the answer data based on a unique ID corresponding to a preset item and generating summarization data by extracting a linguistic expression in the forms of the first language and terms corresponding to the preset item
The above limitations as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by a “processor of a server” nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, and mental processes. For example, but for the “processor of a server” language, the limitations describe a system for generating medical exam data (i.e., medical questions), obtaining a language selection from a user terminal (i.e., an observation of data on a user terminal), obtaining answer data corresponding to the selected language (i.e., receiving answers to the medical questions), classifying the answer data based on a unique ID corresponding to an preset item (i.e., classification of data), and generating summarization data by extracting a linguistic expression in the forms of the selected language and terms corresponding to the preset item (i.e., providing a diagnosis based on the answers, in the selected language) . The limitations describe the management of personal behavior, as well as actions that can be performed mentally or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a “processor of a server” to perform the steps. These additional element are recited at a high level of generality (see at least Para [00062]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. The claims also recite being “artificial intelligence (AI) based” and a “user terminal”. However these merely serve to place the judicial exception into a computer environment. 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 therefore still directed to an abstract idea.
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 element of using a “processor of a server” to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Independent claims 11 and 14 feature limitations similar to those of claim 1, but for the recitation of a “processor of an apparatus” (Claim 11), a “storage unit in which at least one program instruction is stored”, and a “processor configured to perform the at least one program instruction” (Claim 14) to perform the steps, and are therefore also found to be directed to an abstract idea without significantly more.
Claims 2-10 are dependent on claim 1, and include all the limitations of claim 1. Claims 12-13 are dependent on claim 11, and include all the limitations of claim 11. Claims 15-16 are dependent on claim 14, and include all the limitations of claim 14. Therefore, they are also found to be directed to the same abstract idea. Claim 3 features the additional limitation of “the generating of the sentence in the first language based on the language selection information by connecting the extracted terms comprises AI training; and the AI training comprises performing AI training by using all terms extracted for each preset item as input data and using a sentence completed in the first language as output data”. However, the “AI training” is recited at a high level of generality (i.e. apply it), resulting in the “AI training” being merely used as a tool to implement the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The remaining dependent claims have not been found to integrate the judicial exception into a practical application, or provide significantly more than the abstract idea since they merely further narrow the abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4 and 6-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh (US 2021/0287800) in view of Ada – check your health1, available August 4th, 2022, hereinafter referred to as Ada
Regarding claim 1, Ghosh discloses An artificial intelligence (AI)-based multilingual medical examination summarization method being performed by a processor of a server, the method comprising:
generating medical examination data to which unique IDs have been assigned, respectively, wherein the medical examination data comprise user's personal information and symptom and/or disease-related data of the user; (See at least Para. [0070] – “The most probable diagnosis arrived at from the questions generated and answers received is now fed to the clinical knowledge graph (106A) to search out a treatment and recommend a medication for the diagnosed ailment and provide the information to the patient.”, as well as Fig. 6 which features nodes featuring questions related to symptoms (i.e., a unique ID) of a user. The Examiner asserts that symptoms of a user may also be considered as personal information.)
Ghosh does not fully disclose obtaining language selection information that is information of a first language, selected among preset languages, from user terminal; (See Para. [0021] – “The artificial intelligence is executed by the processing systems with software that is able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages (e.g., natural language to machine language and vice versa). The personal medical-bot typically includes a natural language translator.” While this implies that the system of Ghosh is multilingual, Ghosh does not explicitly disclose the selection of a language from among preset languages. See Ada – “Assessments in 7 languages – choose your language and change it from the settings at any point: English, German, French, Swahili, Portuguese, Spanish, or Romanian.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Ghosh to utilize the teachings of Ghosh since both Ghosh and Ada are in the same field of endeavor (i.e., providing a diagnosis based on input user symptoms), and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Ghosh discloses obtaining answer data for the medical examination data corresponding to the language selection information from the user terminal; (See at least Paras. [0028] – “The IPMB 100 includes a user input module (101) that is a natural language input module for receiving natural language input from the patient user.”, and [0029] – “The received inputs are then fed to the clinical language understanding module (103).”)
Ghosh also discloses classifying all of the answer data based on a unique ID corresponding to a preset item (See at least Para. [0031] – “ and generating summarization data by extracting a linguistic expression in the forms of the first language and terms corresponding to the preset item. (See at least Para. [0031] – “For example, as shown in Table 2, the sentence is "I have belly pain with continuous fever". Here, the phrases "belly pain" and "continuous fever" are symptoms as described in the domain of medicine. Accordingly, a symptom can be represented by the domain tag-"symp". Further, (an IOB or IOD (In/Out/ Begin or Descriptor representation) tagging method is used where B-symp denotes the beginning of a symptom tag, I-symp denotes being inside the symptom tag, and O denotes anything outside of these types. Thus in table 2, "belly" is labelled as "B or D-symp", "pain" is labelled as "I-symp", and non-symptom words are labelled as "O".”, Para. [0036] – “The process continues with the received input being processed to understand the clinical terminology and usage within the natural language input (202). The inputs are then converted into a standardized form (203). This standardized form is then used for slot filling (204) to identify the digitizable elements in the standardized input form, as discussed above with reference to, for example, Table 2.”, and Para. [0041] – “Answers from the patient user is received (210) and it is fed back for processing through the steps (202) to (207) until the cumulative data input into the clinical knowledge graph is sufficient to arrive at a diagnosis at (207). Once the data availability for convergence to a diagnosis to be achieved, it is used to generate a diagnosis.”
Regarding claim 2, Ghosh discloses The method of claim 1, wherein the unique ID comprises:
a first classification code to primarily classify the medical examination data into a plurality of types; and
a second classification code to secondarily classify the primarily classified medical examination data into a plurality of types corresponding to each of the first classification codes.
(See at least Fig. 6, Item 604 – “Stomach pain” which equates to a first classification code, and Item 605 – “only intermittent Stomach pain?” which equates to a second classification corresponding to the first classification code.)
Regarding claim 3, Ghosh discloses The method of claim 2, wherein the generating of the summarization data comprises:
classifying all of the answer data based on the first classification code corresponding to the preset item;
classifying and arranging the answer data that have been classified for each preset item based on the second classification code;
(See at least Para. [0069] – “In the example shown in FIG. 6, the user provides an initial input of bloating (606) and stomach pain (604). Initially, the possible linked diagnosis set, at a top level within the knowledge graph 106A, are identified using the symptom inputs provided by the user. In this example, the diagnostic set include any of typhoid (611), food poisoning (612), gastritis (613), and irritable-bowel syndrome (614). By further eliciting the information that vomiting (603) is not a current symptom by the patient in response Al ( 408) to query Ql ( 401) does not help eliminate any of the possible four diagnoses, but the answer A2 ( 409) that there is no fever to query Q2 (402), enables typhoid (611) to be eliminated as a diagnosis as the vector linkage from the symptom fever (601) to diagnosis typhoid (611) is negated. The third query Q3 ( 403) is for clarifying the initial input stomach pain since typhoid (611) has been eliminated as a possible diagnosis, and provides the answer A3 (410) that the 'pain is intermittent,' which eliminates food poisoning (612) as a diagnosis, leaving only gastritis (613) and irritable bowel syndrome (614) as possibilities.” In other words, the queries allow the answers to be “classified” as directed toward a particular illness.
extracting the classified answer data in the forms of the first language corresponding to the language selection information and the terms corresponding to the preset item; and
generating a sentence in the first language based on the language selection information by connecting the extracted terms.
(See at least Para. [0055] – “At this point the data extracted from the clinical knowledge graph is input into the slot fill to convert to natural language by the policy/learning module (105) to be presented to the user by the clinical language presentation module (107). In addition to the diagnosis and treatment arrived at a health score and disease risk value are also extracted from the clinical knowledge graph and converted to natural language format to be presented to the patient.”)
Regarding claim 4, Ghosh discloses The method of claim 3, wherein the extracting of the classified answer data in the forms of the first language corresponding to the language selection information and the terms corresponding to the preset item comprises extracting the answer data in the first language corresponding to the language selection information and the form of the term corresponding to the preset item, among a plurality of pre-stored linguistic expressions corresponding to the unique ID of the answer data. (See at least Para. [0031] – “Here, the phrases "belly pain" and "continuous fever" are symptoms as described in the domain of medicine. Accordingly, a symptom can be represented by the domain tag-"symp". Further, (an IOB or IOD (In/Out/Begin or Descriptor representation) tagging method is used where B-symp denotes the beginning of a symptom tag, I-symp denotes being inside the symptom tag, and O denotes anything outside of these types. Thus in table 2, "belly" is labelled as "B or D-symp", "pain" is labelled as "I-symp", and non-symptom words are labelled as "O".”, and Para. [0036] – “The process continues with the received input being processed to understand the clinical terminology and usage within the natural language input (202). The inputs are then converted into a standardized form (203). This standardized form is then used for slot filling (204) to identify the digitizable elements in the standardized input form, as discussed above with reference to, for example, Table 2.”
Regarding claim 6, Ghosh discloses The method of claim 2, wherein the first classification code is a code to classify the medical examination data as one of an independent data type that is a type in which an additional description is required, a dependent data type that is a type in which the independent data type is described, a personal data type for the user's personal information, and other data type that is a type that does not require an additional description and in which the independent data type is not described. (See at least Fig. 6, Item 605 – “only intermittent Stomach pain?” which describes “Stomach pain” (e.g., an independent data type).”
Regarding claim 7, Ghosh discloses The method of claim 6, wherein the second classification code is a code to classify the primarily classified medical examination data into a data type for a plurality of pieces of aspect information related to the types classified by the first classification code, respectively. (See at least Para. [0031] – “Here, the phrases "belly pain" and "continuous fever" are symptoms as described in the domain of medicine. Accordingly, a symptom can be represented by the domain tag-"symp". Further, (an IOB or IOD (In/Out/Begin or Descriptor representation) tagging method is used where B-symp denotes the beginning of a symptom tag, I-symp denotes being inside the symptom tag, and O denotes anything outside of these types. Thus in table 2, "belly" is labelled as "B or D-symp", "pain" is labelled as "I-symp", and non-symptom words are labelled as "O".”
Regarding claim 8, Ghosh does not explicitly disclose The method of claim 1, further comprising translating the summarization data, wherein the translating of the summarization data comprises:
obtaining, from the user terminal, translation request information that is a request to translate the summarization data from the first language to a second language except the first language, among the preset languages;
extracting the answer data in a form of terms of the second language corresponding to the unique ID of the answer data based on the translation request information; and
generating a sentence in the second language by connecting the extracted terms in the second language.
See Ada – “Assessments in 7 languages – choose your language and change it from the settings at any point: English, German, French, Swahili, Portuguese, Spanish, or Romanian.”
Regarding claim 9, Ghosh discloses The method of claim 1, wherein the obtaining of the answer data comprises:
distinguishing between first answer information that is selected by the user and second answer information that is not selected, within the medical examination data; and
obtaining, from the user terminal, answer data comprising the first answer information and/or the second answer information for the medical examination data.
See at least Fig. 4, which features a series of yes/no answers provided by a user.
Regarding claim 10, Ghosh discloses The method of claim 1, wherein the obtaining of the answer data comprises:
obtaining information on a first symptom of the user from the user terminal;
obtaining, from the user terminal, information on a second symptom of the user that is a symptom accompanying the first symptom;
classifying question information regarding the user, among pieces of pre-stored question information based on the information obtained from the user terminal;
transmitting the classified question information to the user terminal; and
obtaining answer information for the question information from the user terminal.
See at least Fig. 4, which features a user providing a first and second symptom (see Stomach pain and Bloating), as well as a series of questions provided to the user based on the provided symptoms, wherein the user provides at least answers of yes or no.
Claims 11 and 14 feature limitations similar to those of claim 1, and are therefore rejected using the same rationale.
Claims 12 and 15 feature limitations similar to those of claim 2, and are therefore rejected using the same rationale.
Claims 13 and 16 feature limitations similar to those of claim 3, and are therefore rejected using the same rationale.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh (US 2021/0287800) in view of Ada – check your health, available August 4th, 2022, hereinafter referred to as Ada, and in further view of Pan (US 2024/0193377).
Regarding claim 5, Ghosh and Ada do not explicitly disclose The method of claim 3, wherein:
the generating of the sentence in the first language based on the language selection information by connecting the extracted terms comprises AI training, and
the Al training comprises performing AI training by using all terms extracted for each preset item as input data and using a sentence completed in the first language as output data.
(While Ghosh discloses the use of a medical-bot with AI capability (see at least Para. [0008]), Ghosh does not explicitly disclose AI training. See Pan, Para. [0049] – “In some embodiments, the natural language processing task may be question answering in which a question and a body of text, i.e., a text passage, containing an answer to the question are input into the task-modified multilingual machine learning model 222. In response, the task-modified multilingual machine learning model 222 finds the answer within the body of the text and gives as output the answer. This question answering text is helpful for improving the function of automated chatbots which interact with persons seeking questions. With this question and answer, given a question and a passage that are input into the trained model, the trained model extracts a text span from the passage that contains an answer to the question. An evaluation dataset may be applied for this extracting of question and answer that covers multiple languages, e.g., that covers seven different languages.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Ghosh and Ada to utilize the teachings of Pan since it may improve the function of the medical bot of Ghosh (see Pan Para. [0049]).
Conclusion
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
1 Found at https://web.archive.org/web/20220804210202/https://apps.apple.com/us/app/ada-check-your-health/id1099986434