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 in reply to the RCE filed on 03/11/2026.
Claims 1, 16 have been amended and are hereby entered.
Claims 12-14 were previously canceled.
Claims 1-11, 15-16 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/11/2026 has been entered.
Foreign Priority/Priority Date
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP-2023-042754, filed on March 17, 2023. Accordingly, a priority date of 03/17/23 has been given to this application.
Claim Objections
Claims 1, 16 objected to because of the following informalities: Claim 1 as amended recites the limitation “the machine-readable clinical item” in lines 21-22 which does not have true antecedent basis. Examiner is interpreting this to be referring to, and have antecedent basis in, “a machine-readable item” recited by line 19. Examiner recommends streamlining terminology across all claims to recite either “machine-readable item” or “machine-readable clinical item” for improved coherence and consistency of the claims. Claims 2-10, 15 depend from Claim 1 and are subsequently objected to. Claim 16 is objected to for the same reason as Claim 1. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-11, 15-16 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The newly added recitations of “machine-readable item” and “machine-readable clinical item” within Claims 1 and 16 appear to constitute new matter. In particular, Applicant does not point to, nor was Examiner able to find support for this newly added language within the specification as originally filed. The specification does not appear to include instances of machine(-)readable, computer(-)readable, or any terms that would fall into these categories (QR, quick response, bar code, RFID, etc.). As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed spec and claims and/or clarify claim language for what is supported in the specification.
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 1-1, 15-16 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-11, 15-16 are drawn to a method, which is within the four statutory categories. Claims 1-11, 15-16 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
for each patient of a plurality of patients, creating structured data in a standardized format that is applicable across multiple patients by transforming each set of the unstructured digital medical information, the transforming including:
executing pre-configured pattern-matching including morphemic and contextual parsing to identify a text segment in the set of unstructured digital medical information that matches one or more pre-registered clinical linguistic patterns, wherein each clinical linguistic pattern includes at least one variable corresponding to a clinical measurement value in the text segment
extracting the clinical measurement value from the text segment;
mapping the clinical measurement value by executing a threshold-based rule to generate an item indicating a symptom and an associated value indicating a presence of the symptom; and
generating the structured data including the clinical item and the associated value
creating training data associated with the plurality of patients based on the structured data for each patient; and
training a model using the training data to identify at least one candidate disease that affects a respective patient
These steps amount to managing personal behavior or relationships or interactions between people and therefore recite certain methods of organizing human activity. Creating structured data in a standardized format by transforming medical information by identifying a text segment matching a pre-registered pattern, extracting a measurement value from the text segment, mapping the text segment to indicate a symptom and value based on a threshold rule, and generating the structured data including a clinical item and associated value, and creating training data associated with the plurality of patients based on the structured data, are personal behaviors that may be performed by personnel working in the healthcare field. Training a model using the training data to identify at least one candidate disease that affects a respective patient is also certain methods of organizing human activity, as utilizing the training embodiments offered in the instant specification ([0034], [0035]) amounts to applying data to an algorithm and reporting the results. The type of training utilized by the claimed invention is not described by the Applicant. As such the Examiner is required to analyze the training step given the broadest reasonable interpretation. The training of the model is considered to be part of the abstract idea because it falls under data manipulations that humans perform and thus are part of personal behaviors that may be performed by healthcare personnel.
Independent claim 16 recites similar limitations and also recites an abstract idea under the same analysis.
Claims 1 and 16 is therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
“digital” medical information
“machine learning” model
a processor as implementing the steps of executing a pre-configured pattern-matching operation including morphemic and contextual parsing operation to identify a text segment in the set of unstructured medical information that matches one or more pre-registered clinical linguistic patterns, wherein each clinical linguistic pattern includes at least one variable corresponding to a clinical measurement value in the text segment; extracting the clinical measurement value from the text segment; mapping the clinical measurement value by executing a threshold- based rule to generate an item indicating a symptom and an associated value indicating a presence of the symptom; and generating the structured data including the clinical item and the associated value
pre-configured pattern-matching operation including morphemic and contextual parsing operation
machine-readable (clinical) item
a medical service support system comprising: at least one processor; and a storage where a program is stored, wherein the program causes the at least one processor to implement the steps of the abstract idea (Claim 16)
The broad recitation of general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools. Recitation of “digital” medical information is understood to amount to medical information in electronic format, e.g., EMR’s ([0025], “electronic medical chart or the like”). Regarding recitation of “machine learning”, Applicant’s specification only discusses machine learning at a high level (e.g., paras. [0035], [0048]) and as such, this element only amounts to applying the abstract idea on a general purpose computer ([0031], the medical service support system is configured, for example, based on a “personal computer”).
Regarding the processor, no particulars of the processor appear to be disclosed. Therefore, this is understood to be a general purpose computer processor on a general purpose computing device per paras. [0031] (“Medical service support system 100 is configured, for example, based on a personal computer”) and [0032] (“Medical service support system 100 includes a processor 101, a memory 200, and an input and output port 300”).
Regarding a “machine readable” item, the specification does not appear to include any disclosure of machine readable items, computer readable items, or anything that would read on the broadest reasonable interpretation of machine readable item (e.g., quick response, QR, bar code, etc.). Therefore, this element is given its broadest reasonable interpretation as applying the abstract idea on a general purpose computer, e.g., using a computer to generate a data item electronically. Regarding “operations”, no particulars are disclosed by the specification; therefore, this is understood to amount to mere instructions to apply the abstract idea on a computer, e.g., performing the steps of pattern matching and contextual parsing using a general purpose computer per [0030]-[0032].
Regarding a medical service support system comprising: at least one processor; and a storage where a program is stored, wherein the program causes the at least one processor, per paras. [0030]-[0032], this is understood to be a this is understood to be a general purpose computing device (“a personal computer”) with general purpose components functioning in its ordinary capacity.
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 1 additionally recites
for each patient of a plurality of patients, collecting a set of unstructured digital medical information associated with each patient from a database in free text format, wherein the unstructured digital medical information is in the free text format
This step only amounts to insignificant extra-solution activity in the form of mere data gathering, as it amounts to obtaining the data to use when performing the abstract idea.
These elements in sections A and B above are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Claim 1 and Claim 16, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1 and 16 only recites the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer are not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Likewise, as explained above, the step of for each patient of a plurality of patients, collecting a set of digital medical information associated with each patient from a database in free text format, only amounts to insignificant extra-solution activity in the form of mere data gathering.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.0S(d)
In addition to amounting to insignificant extra-solution activity the elements in Section B above constitute well-understood, routine and conventional activity. The step of, for each patient of a plurality of patients, collecting a set of unstructured digital medical information associated with each patient from a database in free text format, wherein the unstructured digital medical information is in the free text format, only amounts to receiving or transmitting data over a network and/or storing/retrieving data in memory, which have been previously held to be well-understood, routine and conventional when claimed at a high level of generality or as insignificant extra-solution activity. See MPEP 2106.05(d)(II).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their
collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims:
Claim 2 recites limitations pertaining to obtaining disease information on a disease from which a patient may be suffered, based on patient information provided by a primary doctor for the patient; determining a reception of a matching request has been obtained from the primary doctor; identifying at least one specialist from among a plurality of specialists in response to the matching request from the primary doctor; and providing the at least one identified specialist, with the obtained disease information and the patient information, which are also certain methods of organizing human activity including managing personal behaviors, as these are all personal behaviors that may be performed by healthcare providers/personnel working in the healthcare space. Claim 2 further recites wherein the identifying at least one specialist includes identifying candidates for the specialist based on registration information of each of the plurality of specialists and the matching request, presenting the candidates to the primary doctor, and obtaining from the primary doctor, designation of the at least one specialist from the candidates, which is also certain methods of organizing human activity including managing personal behavior, as personnel working in the healthcare field could present candidate information on a plurality of specialists to the primary doctor and obtain the primary doctor’s designation of at least one specialist from the candidates. Claim 2 also recites “communicating with a terminal to display the identified at least one candidate disease” which comprises insignificant extra-solution activity in the form of insignificant application, e.g., outputting the result of Claim 1 (at least one candidate disease that affects a patient) after performing the abstract idea (identifying the at least one candidate disease that affects a patient. In addition to amounting to insignificant extra-solution activity, the above limitations also constitute well-understood, routine and conventional activity in the form of receiving/transmitting data over a network. These types of activities have been recognized by the courts as well-understood, routine and conventional activity when claimed as insignificant extra-solution activity. See MPEP 2106.05(d). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3 recites limitations pertaining to wherein the set of digital medical information includes disease information on a disease from which a patient may be suffering, the registration information includes information that identifies an area of specialization, and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that information corresponding to the disease information is registered as the area of specialization of the specialist, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4 recites limitations pertaining to wherein the area of specialization includes at least one of a clinical division and a type of a disease, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5 recites limitations pertaining to wherein the registration information includes information that identifies date and time when each specialist can attend, and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that date and time when the specialist can attend includes corresponding date and time in the matching request, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 6 recites limitations pertaining to wherein the registration information includes information that identifies a time period required by each specialist to respond, and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that the information that identifies the time period required by the specialist to respond satisfies a request for the time period in the matching request, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 7 recites limitations pertaining to wherein the registration information includes information that identifies the number of primary doctors to which each specialist can attend in a given period, and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that the number of primary doctors to which the specialist have attended in the given period is smaller than the number of primary doctors to which the specialist can attend, at time when the matching request is obtained, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 8 recites limitations pertaining to wherein the registration information includes information that identifies years of experience of each specialist, and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who has the years of experience equal to or longer than the years of experience identified in the matching request, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 9 recites limitations pertaining to wherein the registration information includes information that identifies the number of primary doctors to which each specialist is attending, and the identifying at least one specialist includes identifying the at least one specialist based on the number of primary doctors to which each specialist is attending at a time point when the matching request is obtained, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 10 recites limitations pertaining to wherein the registration information includes information that identifies evaluation of each specialist, and the identifying at least one specialist includes identifying the at least one specialist based on evaluation of each specialist, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 11 recites limitations pertaining to wherein the registration information includes position information of each specialist, and the identifying at least one specialist includes identifying the at least one specialist based on position information of the primary doctor from which the matching request has been obtained and the position information of each specialist, which further limits the scope of the abstract idea as set out above. This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 15 recites a recording medium having a medical service support program stored thereon in a non-transitory manner, the medical service support program causing at least one processor to perform the method according to claim 1 by being executed by the at least one processor, which comprises the additional elements of a recording medium having a medical service support program stored thereon in a non-transitory manner and a processor. Per specification paragraphs [0009], [0031], [0032], these are understood to be general purpose computing components functioning in their ordinary capacity to implement the steps of the abstract idea (e.g., the steps of Claim 1), which only amounts to mere instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). This is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Dependent claim 15 recites additional subject matter which amounts to additional elements which only amounts to invoking computers as a tool to perform the abstract idea. See MPEP 2106.05(f). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent claims 2-11, 15, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-11, 15-16 fail the Subject Matter Eligibility Test and are consequently rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 15, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1) and further in view of Seo et. al. (US Publication 20230386491A1).
Regarding Claim 1, Leventhal discloses:
A method for training a machine learning model for at least one candidate disease identification (Abstract teaches on a method for building a health predictive model using a plurality of electronic medical records representing a plurality of electronic medical cases; the predictive model can be trained based on feature vectors by using diagnoses as category labels of the training; personalized and highly relevant medical advice can be provided for a user), comprising:
for each patient of a plurality of patients, collecting a set of unstructured digital medical information associated with each patient from a database in free text format, wherein the unstructured digital medical information is in the free text format ([0059] teaching on training using one or more integrated health records which may include unstructured data such as “text data doctor notes” – interpreted as “unstructured” medical information which is ”free text”; per [0130]-0139], “electronic medical records” including unstructured free text information including doctor appointment notes in free text capturing patient complaints, doctor examination, and observations and imaging test free text notes; [0078] further teaches on collecting free text input during patient-physician dialogue, including “online” (further teaches on “digital”) which can include human narration describing one or more symptoms, medical history, attributes, etc.; Examiner interprets captured free text from a dialogue to read on “unstructured” digital medical information; free text may also be responses to questions in health conversation or text input generated in free form, e.g., one or more sentences or paragraphs in human narration describing one or more symptoms, attributes, medical history, etc.); and
creating structured data in a standardized format that is applicable across multiple patients by transforming each set of the unstructured digital medical information, the transforming including: ([0130]-[0139] as discussed in preceding limitation teach on receiving unstructured data; [0140]-[0145] teach on performing data cleaning operations received data; [0146]-[0158] teach on creating structured data using NLP feature extraction (transformation) which are ultimately used to create a data set for model building (e.g., training); [0264] further describes the tagging process; a word can be marked as a symptom, condition (disease), attribute (severity, location, how long) and value (severe, mild, one side, both sides); [0082] further teaches on the server system creating a normalized (“standardized”) ‘clean’ dataset from doctor notes (unstructured digital medical information), using a canonical ontology (synonymous with applying a transformation to create structured data); having a clean normalized dataset includes “(1) having a common ontology used for normalizing the medical data in the various medical records; (2) extracting the medical information from textual notes included in medical records; (3) being able to perform such operations on a very large number of medical records from one or more sources; and (4) generating normalized data capturing medical records in the form of case feature vectors that can be used for training machine learning models. The creation of such structured data can generate a general framework for many decision support tools to clinicians”);
executing, by a processor, pre-configured pattern-matching operation including ([0257] teaches on the tagging process creating a few database tables that link records to SAV tags; the relationship between symptoms, attribute and values is also stored in the database; the example of “value “for 3 days” is a value of the attribute “how long” for the symptom “headache”” is provided, where Examiner interprets the combination of attribute “how long” with symptom “headache” to read on a “clinical linguistic pattern” that is pre-registered as it is stored in a database; [0268] teaches on searching for known tags in new visit records and using a “string matching” approach – interpreted as a parsing operation; [0270] teaches on the auto-tagger keeping a “table of synonyms”, e.g., deducing that “<organ>pain” is equivalent to “<organ>ache” (table of synonyms/recognizing that organ pain/organ ache are the same is interpreted as a identifying a text segment matching one or more pre-registered clinical linguistic pattern (e.g., <organ> ache is the linguistic pattern); as the auto tagger is an automated process ([0260] it is interpreted as being performed by a processor (see [0382] teaching on system architecture); [0267] teaches on using the auto tagger to identify “knee pain” as a symptom from dialogue/notes and identifying the attribute (variable) of “how long” having a value “3 days” (a clinical measurement value) corresponding to the text segment pertaining to knee pain);
extracting, by the processor, the clinical measurement value from the text segment ([0267] teaches on auto tagger (an automated process, interpreted as being performed by a processor) identifying “knee pain” as a symptom with a value of “3 days” (clinical measurement value; see Fig 8 example 3 where “3 days” is highlighted);
mapping, by the processor, the clinical measurement value to generate a machine-readable item indicating a symptom and an associated value indicating a presence of the symptom (Fig. 8 and [0267] teaching on identifying “knee pain” as symptom with value “3 days” for clinical attribute of “how long”; the auto tagger adds a tagged visit record with the found tags and adds the new relationship of knee pain having attribute “how long” with “3 days” – interpreted as mapping an item indicating a symptom and an associated value indicating a presence of the symptom, e.g., present for 3 days; per [0257] the relationship between symptoms, values and attributes are stored in a database which is interpreted as reading on the broadest reasonable interpretation of “machine readable item” (as discussed above in 112(a) section, the specification does not appear to have disclosure of machine readable items; per [0029] of instant specification, Examiner is interpreting a data table to read on broadest reasonable interpretation of this element as [0029] discloses that Fig. 2 (a data table) is a diagram showing generated structured data); [0260] further teaches on auto tagger using tagged visit records database with symptom tags, attribute tags, and value tags to search the text fields of a larger set of non-tagged visit records); and
generating, by the processor, the structured data including the machine-readable clinical item and the associated value ([0267]-[0272] teach on the process of tagging data with records each linked to symptom, attribute and tag values (interpreted as reading on broadest reasonable interpretation of machine-readable clinical item and associated value as discussed in preceding limitation); after the automated tagging iterations, the system has several thousand or millions of tagged visit records corresponding to a known diagnosis linked to symptom, attribute, value; [0274]-[0275] teach on the auto-tagging and post-tagging producing a large set of tagged visit vectors; the symptom-value-attribute objects may be stored in a relational database or graph database – interpreted as “machine readable” clinical item and associated value);
creating training data associated with the plurality of patients based on the structured data for each patient ([0267]-[0272] teach on the process of tagging data with records each linked to symptom, attribute and tag values (interpreted as machine-readable clinical item and associated value); [0273] teaches on after tagging iterations, post-tagging processing is performed which can allow training of the machine learning model; [0284]-[0286] further teach on generating tagged records and post-tagging processing which can produce final records used for training a model – understood to indicate that training data has been created if the data records are used for training the model); and
training a machine learning model using the training data to identify at least one candidate disease that affects a respective patient ([0071] teaches on training a model; the server system can create a mathematical and learning model that, given an input of SAV’s, can provide an output of “the most probable conditions”; conditions are interpreted as being synonymous with “disease” in the context of [0010], [0196], [0264], equating medical condition/condition with “disease”; [0082] teaches on the clean normalized data set, which includes “In some embodiments, creating a clean normalized dataset includes: “generating normalized data capturing medical records in the form of case feature vectors that can be used for training machine learning models” – interpreted as training the ML model using the training data; [0083] further teaches on training the model using many records).
Leventhal does not explicitly disclose the following, but Gluck, which is directed to a method and apparatus for determining and presenting information regarding medical condition likelihood, teaches:
mapping a clinical measurement value by executing a threshold- based rule to generate an item indicating a [factor] and an associated value indicating a presence of the [diagnosis] ([0071] teaches on the computer system employing a threshold value for numerical range test results (“clinical measurement value”) whereby values over the threshold are assigned one binary value (position) and values below the threshold are assigned a different binary value (negative); see Fig. 4 including tests (factors) with results in the second column, where “initial probability” has NEG or POS shown for different diagnoses (e.g., acute coronary syndrome vs. pulmonary embolism).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed, to modify Leventhal with these teachings of Gluck, to incorporate the threshold-based rule of Gluck into the system of Leventhal, with the motivation of converting a numerical test result to a binary (e.g., negative or positive) result for purposes of analysis (Gluck [0093]).
Leventhal/Gluck do not teach the following, but Seo, which is directed to an artificial intelligence device, teaches:
morphemic parsing operation ([0046] teaches on performing a morpheme analysis step by NLP server)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed, to modify Leventhal/Gluck with these teachings of Seo to incorporate a step of performing a morphemic parsing operation, with the motivation of classifying text data into morpheme units, which are the smallest units having meaning and classifying individual morphemes for analysis (Seo [0047]).
Regarding Claim 15, Leventhal/Gluck/Seo teach the limitations of Claim 1. Leventhal further discloses a recording medium having a medical service support program stored thereon in a non-transitory manner, the medical service support program causing at least one processor to perform the medical service support method according to claim 1 by being executed by the at least one processor (paras. [0382]-[0384], teaching on system architecture).
Regarding Claim 16, Leventhal/Gluck/Seo teach the limitations of Claim 1. Claim 16 recites the same or substantially similar limitations as Claim 1, and the discussion above with respect to Claim 1 is equally applicable to Claim 16. Claim 16 additionally recites the following which is also taught by Leventhal a medical service support system comprising: at least one processor; and a storage where a program is stored, wherein the program causes the at least one processor (paras. [0382]-[0384], teaching on system architecture).
Claim(s) 2, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1) and further in view of Seo et. al. (US Publication 20230386491A1) as applied to Claim 1 above in view of Tavakol et. al. (US Publication 20210304141A1).
Regarding Claim 2, Leventhal/Gluck/Seo teach the limitations of Claim 1. Leventhal further discloses:
communicating with a terminal to display the identified at least one candidate disease ([0063] teaches on the technical environment; the technical solution can include “display of various predicted results using the model”; [0071] teaches on outputting the most probable conditions; see [0200]/Table 1 for exemplary code, 9a and b respectively teach on calculating conditions probabilities and displaying conditions whose probability exceeds a condition probability threshold – determining which condition to display; per [0049] and Figs. 15, 16, it is understood that the candidate disease/condition may be displayed via smartphone, e.g., a terminal).
Leventhal does not disclose the following, but Tavakol, which is directed to a method for managing physician referrals, teaches:
receiving a matching request for matching with a specialist for each candidate disease ([0217] teaches on the doctor requesting availability of applicable doctors for a patient requiring a referral; [0218]-[0219] teach on the PC sending the request to an aggregator server which searches a connected database; if the aggregator searches the database to return requested information to the doctor ([0220]), it is interpreted as reading on the claim language that the system receives a matching request from the primary doctor has been received; [0173] teaches on the physician entering search/filtering criteria and clicking the search button to initiate the search of the aggregator’s database based on the entered filtering information – interpreted as determining a matching request has been obtained from the primary doctor if the system filters based on entered criteria); and
in response to the matching request, identifying at least one specialist from among a plurality of specialists ([0220] teaches on the server returning the requested information to the doctor’s PC in response to the request described in [0216]-[0217]; [0173] teaches on the physician initiating a search of referral providers based on entered filtering criteria; below the filtering criteria window is a results window which contains a list of potential receiving physicians that satisfy the filtering criteria; see Fig. 3 which shows “Find a doctor” with various drop-down boxes and a list of 3 potential receiving physicians meeting the filter criteria),
wherein the identifying at least one specialist from among a plurality of specialists includes identifying candidates for the specialist based on registration information of each of the plurality of specialists and the matching request ([0027]-[0029] teach on an online physician referral system in which a central managed database contains physician profile data (PPD), which is interpreted as “registration information” of each of the plurality of specialists; a referring physician can access the system via portal to filter the PPD on behalf of a particular patient – filtering to a particular patient’s requirements is interpreted as reading on “matching request”), presenting the candidates to the primary doctor ([0029] teaching on the portal which includes a user interface to provide the filtered PPD and available appointment times to the referring physician), and obtaining from the primary doctor, designation of the at least one specialist from the candidates ([0029] teaches on providing a user interface with filtered PPD data to the referring physician, the user interface enables the referring physician to select and book on-line a referral appointment on behalf of the patient with one of the physicians based on a filtered combination of data including PPD, e.g., profile/registration data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo with these teachings of Tavakol, so that after the machine learning model of Leventhal outputs an identified candidate disease for a patient, the system identifies at least one specialist based on a matching request, in which the candidates are identified based on their registration information and presented to the referring primary doctor, upon which the primary doctor designates at least one specialist, because many patients are generally unfamiliar with specialized doctors beyond their PCP until medical circumstances necessitate a referral to a specialist; when patients have to arrange a specialist appointment on their own they may find themselves referred to a doctor with little or no availability or who doesn’t accept their insurance (Tavakol [0002]) and with the motivation of enabling the primary doctor to make a selection based on at least one of specialty and procedures performed, specialist affiliation, insurance or payment information, and available appointment times (Tavakol [0103]-[0107]).
Regarding Claim 7, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2. Leventhal does not disclose, but Tavakol further teaches wherein the registration information includes information that identifies the number of primary doctors to which each specialist can attend in a given period ([0168] teaches on each referred physician having the ability to edit their availability; availability can also be synchronized with a doctor’s calendar; this information is available to other medical professionals across the system; see Fig. 3 box 38; Examiner interprets the doctor’s availability to read on registration information including the number of primary doctors to which each specialist can attend in a given time period, where the time period is by day/week as shown in Fig. 3 (e.g., 01/19/20-01/25/20 is the timeframe shown; Examiner interprets each referred patient to be indicative of “number of primary doctors to which each specialist can attend” as each referred patient represents a primary doctor), and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that the number of primary doctors to which the specialist have attended in the given period is smaller than the number of primary doctors to which the specialist can attend, at time when the matching request is obtained ([0173] teaches on the “Find a doctor” page which includes, a results window for filtering results, which includes a separate row of information for each referred physician; a grid display (item 38 on Fig. 3) shows available appointment times for the physician; the referring physician can click on each individual appointment time link to make an appointment on behalf of the patient; see Fig. 3; Examiner interprets display of available appointment times for a referred physician, e.g., 1:45p and 2:30p on Tues 01/19/2010 to indicate that the number of primary doctors to which the specialist can attend has not exceeded the number to which the specialist has attended, as there are still open appointments available. If the number to which the specialist has attended is not smaller than the number to which the specialist can attend, there would not be available appointments shown).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol, to include in the registration information of Tavakol, the information identifying the number of primary doctors to which a specialist can attend, where the identifying at least one specialist includes identifying a specialist who satisfies a condition that the number of primary doctors to which the specialist can attend in the given period is smaller than the number of primary doctors to which they can attend, with the motivation of ensuring a doctor selected will have availability for the referred patient (Tavakol [0107]).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1) and further in view of Seo et. al. (US Publication 20230386491A1), and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Buck et. al. (US Publication 20120010904A1).
Regarding Claim 3, Leventhal/Gluck/Seo/Tavakol teach the limitations of claim 2. Leventhal further discloses wherein the set of digital medical information includes disease information on a disease from which a patient may be suffering ([0130]-[0135] teach on using free text information from EMRs, which may include “doctor visit summary diagnosis” which may be represented as coded International Classification of Diseases and past medical history such as diabetes which is interpreted as a disease),
Leventhal does not disclose, but Tavakol further teaches wherein the registration information includes information that identifies an area of specialization ([0028] teaches on the physician profile data (registration information) including the physician’s specialty).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol so that the registration information of specialists includes information identifying an area of specialization, with the motivation of allowing the referring physician to select a specialist for a patient based on the specialist’s area of specialization (Tavakol [0103]).
Leventhal/Gluck/Seo/Tavakol do not explicitly teach the following, but Buck, which is directed to a method of reverse physician-patient matching for healthcare services, teaches: the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that information corresponding to the disease information is registered as the area of specialization of the specialist ([0005] teaches on using the system to select a provider for a specialist visit or expert consultation; [0028] teaches on expert consultations including consultations on diseases; [0070] teaches on matching patient characteristics including diagnosis, interpreted as reading on broadest reasonable interpretation of disease information, to characteristics of the provider including specialty, interpreted as reading on the area of specialization of the specialist).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Buck, so that the identified specialist of Leventhal/Tavakol is identified by satisfying a condition pertaining to the disease information of the patient and the specialty area of the specialist, with the motivation of selecting a patient-provider match that is likely to correlate to a positive outcome (Buck [0070]).
Regarding Claim 4, Leventhal/Gluck/Seo/Tavakol/Buck teach the limitations of Claim 3. Leventhal does not disclose, but Tavakol further teaches wherein the area of specialization includes at least one of a clinical division and a type of a disease (Abstract teaches on filtering available doctors by specialty and sub-specialty (interpreted as “clinical division”); Fig. 9 shows a referral booking screenshot for patient Richard Smith, in which the referred-to physician has a specialty of “Cardiologist”, interpreted as a specialization including a clinical division).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol so that the registration information of specialists includes information pertaining to a clinical division or type of disease as the area of specialization, with the motivation of allowing the referring physician to select a specialist for a patient based on the specialist’s area of specialization/procedures performed (Tavakol [0103]).
Claim(s) 5, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1) further in view of Seo et. al. (US Publication 20230386491A1), and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Wilson et. al. (US Patent 9996666B1).
Regarding Claim 5, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2. Leventhal does not disclose, but Tavakol further teaches wherein the registration information includes information that identifies date and time when each specialist can attend ([0168] teaches on each participating practice having the ability to edit their physicians’ displayed availability; see Fig. 4 which shows available appointment dates and times for referred specialist provider Leigh J. Lachman).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol so that the registration information identifies availability of when each specialist can attend, with the motivation of selecting a specialist based on their displayed available appointment times (Tavakol [0107]).
Leventhal/Gluck/Seo/Tavakol do not explicitly teach the following, but Wilson, which is directed to a physician scheduling system, teaches: the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that date and time when the specialist can attend includes corresponding date and time in the matching request (Col 36 line 46-Col 37 line 11 teach on a matching-engine system receiving a search query from a user of matching engine system; the search query includes a preferred date and time for an appointment and one or more user-specified symptoms; the matching engine identifies a first set of physicians based on the search query and subsequently a second set of physicians, in which the second set is a subset of the first set and includes physicians who have indicated availability at the preferred date and time of the search query).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo /Tavakol with these teachings of Wilson, to enable the referring physician of Tavakol to search for a specialist and be matched to one or more specialists based on a date and time condition, with the motivation of enabling both specialists (providers) and users looking to be referred to a specialist (provider) to provide information regarding their preferred times to be matched for an appointment (Col 32 lines 50-53, Col 33 lines 61-64).
Regarding Claim 11, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2. Leventhal does not disclose, but Tavakol further teaches primary doctor ([0167], primary care provider) and wherein the registration information includes position information of each specialist ([0027]-[0029] teach on an online physician referral system in which a central managed database contains physician profile data (PPD), which is interpreted as “registration information” of each of the plurality of specialists; PPD includes the physician’s specialty and location; location is interpreted as “position information”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol so that the registration information of specialists includes position information, with the motivation of allowing the referring physician to select a specialist for a patient based on the specialist’s area of specialization/procedures performed (Tavakol [0103]).
Leventhal/Gluck/Seo/Tavakol do not explicitly teach the following, but Wilson, which is directed to a physician scheduling system, teaches: the identifying at least one specialist includes identifying the at least one specialist based on position information of the [patient] from which the matching request has been obtained and the position information of each specialist (Col 36 lines 46-50 teach on determining a specialty of physicians to which a patient may be matched, which is interpreted as a specialist; Col 36 lines 58-62 teach on a search query comprising a geographic location of the user; the system may identify a group of physicians based on the respective geographic location of each physician compared to the geographic location of the user; Col 12 line 61-Col 13 line 1 teach on the system identifying only physicians within a preset distance of the location of the user; the user may specify a distance to look for physicians to recommend, e.g., only identify physicians within 25 miles of the user; geographic location/location are both interpreted as reading on “position information”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo /Tavakol with these teachings of Wilson, to enable the referring physician (primary care provider) searching for a specialist of Tavakol to be matched to one or more specialists based on their locations, with the motivation of enabling a primary doctor to refer a patient to a specialist based on their locations so that the patient can visit a specialist in proximity to where they reside or work (Wilson col 12 lines 52-55). While Wilson teaches on matching patient and provider based on patient and provider locations, it would be prima facie obvious to use the same matching system of Wilson for the provider and specialist as Tavakol, as it is merely determining the distance between two different parties when determining a list of recommended providers.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1) further in view of Seo et. al. (US Publication 20230386491A1), and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Athman et. al. (US Publication 20230085426A1).
Regarding Claim 6, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2. Tavakol further discloses wherein the registration information includes information that identifies [provider data] ([0027]-[0029] teach on an online physician referral system in which a central managed database contains physician profile data (PPD), which includes physician’s specialty, location and insurance/payment info, which is interpreted as “registration information” of each of the plurality of specialists), and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who satisfies a condition that the information that identifies [provider data] satisfies a request for the time period in the matching request ([0027]-[0029] teach on an online physician referral system in which a central managed database contains physician profile data (PPD), which includes physician’s specialty, location and insurance/payment info, which is interpreted as “registration information” of each of the plurality of specialists; a referring physician can access the system via portal to filter the PPD on behalf of a particular patient – filtering to a particular patient’s requirements is interpreted as reading on “matching request”).
Leventhal/Gluck/Seo/Tavakol do not explicitly teach the following, but Athman, which is directed to querying multiple sources over a network for medical information, teaches: a time period required by each specialist to respond ([0030] teaches on provider data including the provider’s responsiveness such as turnaround time of past query responses).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Leventhal/Gluck/Seo/Tavakol to incorporate a time period required by each specialist to respond, as taught by Athman, as a since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Tavakol already discloses provider data such as location or specialty, which may be used by a primary care provider to search for a referral specialist meeting those criteria (see e.g. Tavakol [0027]-[0029]). Incorporating provider data such as a time period required by each specialist to respond as taught by Athman would perform their same functions within the system of Leventhal/Gluck/Seo/Tavakol, as it is merely a different type of provider data which may be used as a filtering/searching criteria, making the results predictable to one of ordinary skill in the art (KSR A, MPEP 2143).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over T Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1), further in view of Seo et. al. (US Publication 20230386491A1), and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Schoenberg (US Publication 20090089074A1).
Regarding Claim 8, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2 but do not explicitly teach the following. Schoenberg, which is directed to a system for identifying trusted healthcare providers, teaches: wherein the registration information includes information that identifies years of experience of each specialist ([0122] teaches on a provider establishing a profile which includes information relevant to another user in making the choice to use a particular provider over other providers; information about the provider includes professional certification, demographics, education, publications, etc.; interpreted as “registration information”; [0127] teaches on a provider enrolling and providing his credentials and years in practice – “years of experience”; per [0036], [0046], [0061], [0154], the providers are understood to include “specialists”), and the identifying at least one specialist includes identifying as the at least one specialist, a specialist who has the years of experience equal to or longer than the years of experience identified in the matching request ([0155] teaches on a user searching for a provider based on provider qualifications; searches may be based on the provider’s years of experience; [0156] teaches on displaying a user interface in response to a user’s search for a provider; see Fig. 5B, provider search, which includes box 244b “Years of Experience” as well as “Provider Type”, OB/GYN at 240a, e.g., a specialist in Ob/Gyn; see Fig. 5C which shows a list of doctors matching the specified criteria in Fig. 5B including “with 10-20 years experience”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Schoenberg, to enable the referring physician of Tavakol to search for a specialist and be matched to one or more specialists based on a criteria for years of experience, with the motivation of matching with a specialist provider with preferred specified demographics (Schoenberg [0059]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1), further in view of Seo et. al. (US Publication 20230386491A1), and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Rajasenan (US Patent 8515777B1).
Regarding Claim 9, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2. Leventhal does not disclose the following, but Tavakol further teaches identifying at least one specialist includes identifying the at least one specialist based on the [availability of] each specialist at a time point when the matching request is obtained [0173] teaches on the “Find a doctor” page which includes, a results window for filtering results, which includes a separate row of information for each referred physician; a grid display (item 38 on Fig. 3) shows available appointment times for the physician; the referring physician can click on each individual appointment time link to make an appointment on behalf of the patient; see Fig. 3; Item 28 includes a checkbox to include only doctors with availability in the results; Examiner interprets display of available appointment times for a referred physician, e.g., 1:45p and 2:30p on Tues 01/19/2010 to indicate the availability of each identified specialist at the time point when the matching request was obtained).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Tavakol so that the registration information of specialists includes availability information of each specialist, with the motivation of allowing the referring physician to select a specialist for a patient based on the specialist’s available appointment times (Tavakol [0107]).
Leventhal/Tavakol do not explicitly teach the following. Rajasenan, which is directed to a method for efficient provision of healthcare, teaches: wherein the registration information includes information that identifies the number of [patients] to which each [provider] is attending (Col 8 Lines 18-22 teach on the system analyzing the number of patients a particular healthcare provider is caring for), and the identifying at least one [provider] includes identifying the at least one [provider] based on the number of [patients] to which each [provider] is attending at a time point (Col 5 lines 17-31 teach on the concept of “tipping points” in healthcare such as the maximum number of patients to whom one provider can provide quality healthcare; Col 8 lines 18-25 teach on the system analyzing the number of patients a particular provider is caring for and assigning a patient to another healthcare provider if the number is too high, so the tipping point is eliminated).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the combined teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Rajasenan, to use Rajasenan’s method of determining how many patients (e.g., the primary doctors of Tavakol) a provider (the specialist of Tavakol) is attending to, and to identify providers (specialists of Tavakol) based on this information when the matching request is obtained, with the motivation of ensuring that a provider (specialist) isn’t responsible for too many patients (primary doctors) because a healthcare provider can only provide so much care at a particular quality level before that quality level begins to decrease (Rajasenan Col. 1 lines 50-45). Examiner interprets the Rajasenan reference as being analogous as it is understood that a primary doctor’s request is related to a particular patient and a specialist “attending” to a primary doctor is understood to be serving the patient of the primary doctor.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Leventhal et. al. (US Publication 20200185102A1) in view of Gluck et. al. (US Publication 20200135336A1), further in view of Seo et. al. (US Publication 20230386491A1) and further in view of Tavakol et. al. (US Publication 20210304141A1), as applied to Claim 2 above, and further in view of Karnati et. al. (US Publication 20160379173A1).
Regarding Claim 10, Leventhal/Gluck/Seo/Tavakol teach the limitations of Claim 2 but do not explicitly teach the following. Karnati, which is directed to a medical appointment scheduling system, teaches: wherein the registration information includes information that identifies evaluation of each [provider] ([0060] teaches on the system maintaining practice summary information, which includes information about the practice including provider’s average rating from ratings inputted by patients – practice summary information is interpreted as “registration information”), and the identifying at least one [provider] includes identifying the at least one [provider] based on evaluation of each [provider] ([0064] teaches on performing a provider search with search criteria including “ratings above a certain level, e.g., 3 stars”, which is interpreted as “evaluation of each specialist”; only search results meeting the filter are shown to the user).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the teachings of Leventhal/Gluck/Seo/Tavakol with these teachings of Karnati, to enable the referring physician searching for a specialist (as taught by Tavakol) to search and be matched to one or more specialists based the evaluation of the specialist, with the motivation of compiling ratings of each provider to determine quality/rating parameters so that the user can specify their desired provider rating/quality when searching for a provider (Karnati [0059], [0069]).
Response to Applicant’s Remarks/Arguments
Please note: When referencing page numbers of Applicant’s response, references are to page numbers as printed.
35 USC 101 Rejections
Applicant’s remarks have been fully considered but are not persuasive. Regarding remarks pertaining to Example 39, Claim 1 and the amendments, Examiner respectfully submits that the claims in Example 39 were found to not be directed to any of the enumerated types of abstract ideas and were thus eligible under step 2A – Prong 1 of the Alice Corp. test for subject matter eligibility. MPEP 2106.04(a)(1) states that “examiners should keep in mind that while all inventions at some level embody, use, reflect, rest upon, or apply laws of nature, natural phenomenon, or abstract ideas, not all claims recite an abstract idea” (internal quotations omitted). Example 39 is a hypothetical illustration of this principle. The training, use, and subsequent retraining of the Neural Network model in Example 39 are all functions that are outside of the ambit of an abstract idea (see MPEP 2106.04(a)(1)(vii)). And, while there may be an abstraction present in the collection of data, the remainder of the claim (all the additional elements of the claim) are purely directed to improvements in training Neural Network to detect faces.
Examiner respectfully disagrees that the instant invention is analogous to Example 39. As discussed above in the 101 analysis section, the broadest reasonable interpretation of the claim limitations “creating structured training data in a standardized format…” and subsequent limitations cited by Applicant at page 6, when read in light of th specification, amount to data processing/data calculations that could be performed by a healthcare provider. For example, a healthcare provider could perform pattern-matching to identify a text segment in a set of unstructured medical information that matches a pre-registered clinical linguistic patterns, extract the clinical measurement value from the text segment, map the value to indicate a symptom and associated value indicating presence of the symptom, generate structured data, and create training data. The high level recitation of “training” a machine learning model utilizing the training embodiments offered in the instant specification (see at least [0034] and [0035]) amount to applying data to an algorithm and reporting the results (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or certain methods of organizing human activity of labeling and fitting data to a particular model representation.
Examiner respectfully disagrees with Applicant’s assertion that Claim 1 does not recite certain methods of organizing human activity; MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of personal behaviors that a healthcare provider, with or without the aid of a computer, would perform to transform unstructured medical data into structured medical data to create training data to train a machine learning model to identify at least one candidate disease affecting a patient. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of personal behaviors that a person or persons would follow to transform unstructured medical data into structured medical data to create training data to train a machine learning model to identify at least one candidate disease affecting a patient, the claimed invention is directed to an abstract idea.
In view of Examples 47-49 of the July 2024 Subject Matter Eligibility Examples, Applicant’s claims and arguments are further analyzed in light of Examples 47-49. Per Example 47, Clam 2, the training of a particular machine learning (“ML”) or artificial intelligence (“AI”) model (ANN in the case of Ex. 47) may fall under the abstract idea of a mathematical concept where, given the broadest reasonable interpretation in light of the Specification, the training merely represents mathematical calculations performed on data. Here, Applicant’s Specification does not actually describe how the training is performed, but rather only discloses that the model is trained using training data (see [0034], [0035]), which the Examiner interprets as merely applying data to an algorithm to arrive at a trained model and has categorized this as certain methods of organizing human activity including managing personal behaviors.
The use of the trained ML model in Example 47, Cl. 2 thereafter represents the application of this abstract idea (“apply it”) on a generic computer. This is because the use of the trained ML in the claims does not place any limits on how the trained ML functions. Where “the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished” (see MPEP 2106. 05(f)). Put another way, where the use of trained ML model in a claim does not recite how its functions are actually performed and are merely recited at a high, non-inventive level, the ML itself represents the application of a mathematical concept because no improvement to the ML is claimed. Recitation of training a machine learning model to identify at least one candidate disease is only claiming the idea of a solution or outcome, because the claims, given the broadest reasonable interpretation in light of the Specification, represent generic ML functionality.
Regarding remarks beginning at bottom of page 6 continuing to page 7 regarding integration of the abstract idea into a practical application, the Examiner respectfully disagrees. Applicant has not cited to, nor can Examiner find, evidence in specification as to how the claimed combination of additional elements “increase operation speed”. Regarding “automatically” identifying text segment and extracting a clinical measurement value, Examiner submits that merely automating a process using a computer does not automatically confer subject matter eligibility. (See MPEP 2106.05(a)(I), example (iii) under “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality”). Examiner submits that identifying and extracting a value, removing redundant calculations, and ensuring uniform output may be improvements to the abstract idea itself, but are not technological improvements as they are not improvements provided by one or more additional elements. Please reference MPEP 2106.05(a) which states, “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” Applicant has not provided, nor can Examiner find evidence of, how any of the additional elements identified above in main 101 analysis section are providing an improvement over prior art systems. The additional elements identified above are understood to amount to using a general purpose computer to apply the abstract idea which is not sufficient to integrate the judicial exception into a practical application. Therefore, this argument is not persuasive.
Regarding remarks at page 7 pertaining to “technical solution to a technical problem arising in the field of medical service support system”, Examiner respectfully disagrees with Applicant’s position. MPEP 2106.04(d)(1) states that a practical application may be present where the claimed invention improves another technology. See also MPEP 2106.05(a)(II). Applicant’s claim is confined to a general-purpose computer (see Spec. Para. [0030]-[0032]) and does not recite “another technology.” Because no other technology is recited in the claim, the claim cannot improve another technology (see, e.g., MPEP 2106.05(I)(A)(i) describing an example of an improvement to another technology where the abstract idea implemented on a computer improved the claimed additional element of a rubber molding machine). Applicant’s claimed invention recites the additional element(s) of a processor as implementing the steps of the abstract idea. While these additional elements implement the steps of the abstract idea, there is no indication that these additional elements operate in a manner different than they normally operate. Operating a computing device in the manner it normally operates is insufficient to improve that other technology. As such, these additional elements are not improved through implementation of the abstract idea and a practical application is not present. This argument is not persuasive.
Regarding remarks at page 7 pertaining to “significantly more”, Examiner submits that Applicant has not provided evidence of, nor can Examiner find evidence, in specification as to how the combination of additional elements provides significantly more than the abstract idea. Regarding remarks pertaining to prior art, please see MPEP 2106.05(I) which states, “Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101"). As made clear by the courts, the "novelty” of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." This argument is not persuasive.
For all of the above reasons, Applicant’s arguments are not persuasive.
The rejections of Claims 1-11, 15-16 under 35 USC 101 are maintained.
35 USC 103 Rejections
Applicant’s remarks have been fully considered but are not persuasive. Applicant’s remarks all appear to be directed to the claims as amended. In response to Examiner’s amendments, Examiner has added new citations to the Leventhal reference to read on the broadest reasonable interpretations of the amended limitations pertaining to the data transformation. For example, paras. [0267]-[0270] teach on performing a pattern-matching operation to identify a text segment in unstructured medical information that matches a pre-registered clinical linguistic pattern which includes at least one variable corresponding to a clinical measurement value in the text segment; [0267] and Fig.8 teach on “extracting” the clinical measurement value from the text segment as well as mapping the value to generate a symptom and associated value indicating presence of the symptom; paras. [0267]-[0275] teach on generating the structured data. The Gluck and Seo references have been introduced to teach on particular portions of amended Claim 1 and 16. New grounds of rejection have been necessitated by Applicant’s amendments.
The rejections of Claims 1-11, 15-16 are maintained under 35 USC 103.
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20160048655A1, teaching on automated analysis of clinical text using natural language processing
US Publication 20240312578A1, teaching on a system for monitoring and handling of diseases which compares patient symptom data to a threshold to make decisions regarding the patient’s condition and care
US Publication 20190189253A1, teaching on verifying patient conditions in EMRs based on a medical condition indicator presence
US Publication 20180121618A1, teaching on a system and method for extracting oncological information of prognostic significance from natural language
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682
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