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 .
DETAILED ACTION
Information Disclosure Statement
As required by M.P.E.P. 609 (C), the applicant’s submission of the information Disclosure Statement dated 10/30/2024 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-12 and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rice et al. (US Pub. 2022/0108790).
Regarding independent claims 1, 11 and 20, Rice discloses an allocation system (FIG. 1: a physician recommendation system 100) comprising:
at least one memory (Fig.1: a database 108) storing instructions ([0028]: The server computer(s) may therefore execute the method steps within one or more of the disclosed algorithm by sending instructions, possibly in the form of compiled and executable software code for any of the disclosed software modules, to a processor on the server computer(s)); and
at least one processor configured to access the at least one memory and execute the instructions ([0028]: The processor may then execute these instructions causing the server computer(s) to complete the disclosed method steps) to:
obtain data that includes profiles of doctors and clinical initial information of a patient that arrives at a hospital (Fig. 3A-3H and [0029]: The database 108 may be configured to store data associated with the physician recommendation system 100. The database 108 may be any sort of system capable of storing data, such as a relational database, a database management system, a hierarchical file, a flat file, and the like. And the database 108 may include data relating to one or more physicians, as well as patient preferences and profile data that may later be compared by the recommendation module 106 to identify one or more physicians for the patient. [0048]-[0049]: patient profile data may include healthcare information);
preprocess the obtained data, wherein preprocessing includes a text preprocessing pipeline and word embedding ([0059]: the interface 102 may provide the user with an application program interface to receive third party data feeds which may be downloaded and stored in the database 108 in association with the patient and/or provider profile account. This downloaded data may be parsed, tokenized and/or analyzed to supplement the questionnaire response data according to a category and/or attribute assigned to each data received from, for example, a social media account and/or medical data database. To access the API, the user may provide authentication information in order to access, download and/or store the user's data in database 108. In this way, patients may append their profiles by connecting their social media profiles or electronic health records to their patient or provider profile. Data from these third party providers may auto-populate select profile questions/categories);
allocate a doctor to the patient that arrives at the hospital by using a neural network, wherein the neural network is machine-learned by using graph data of clinical narratives composed by doctors about patients, historical profiles of the patients and the profiles of doctors ([0003]: The present disclosure relates to systems and methods for coordinating physician matching for a patient. More particularly, the disclosure relates to systems and methods for recommending one or more physicians to the patient based on: patient preferences matching physician profiles; existing networks of health care providers evident within the physician profiles, which address predictive health conditions for the patient within the patient's profile; and analyzing the results of the system and methods (e.g., patient and physician satisfaction), using machine learning to assign specific weights to preferences and other factors that determined the most favorable patient/physician matches.); and
output information indicating the allocated doctor (Fig.6A and [0087]: After ranking the physicians at process block 208, the recommendation module 106 may display or output a list of recommended physicians to the user at process block 210. The list of recommended physicians may be displayed on the interface 102, or output for use by an additional software or database application).
Regarding claims 2 and 12, Rice teaches wherein the at least one processor is further configured to execute the instructions to: generate graph data from the preprocessed data, wherein: each node of the graph data is a patient or a doctor, each edge of the graph data is a link between a patient and a doctor, and each link is labeled by the clinical initial information of the patient that arrives at the hospital, and wherein the doctor is allocated based on the generated graph data (Fig.4B and [0075]: the data between patients and providers may be matched by persona matching, as seen in FIG. 4B. Based on their responses to questions or other input provided within the software, patients and providers are both mapped to either a single person or are identified as having tendencies more closely related to up to two personas. Patient
personas are then aligned with compatible physician personas during the matching process. Fig.4C and [0076]: the data between patients and providers may be matched is by health needs matching, as seen in FIG. 4C. Health needs matching compares data elements from a patient's health care profile (e.g., medical conditions, health care needs, health plan, etc.) with related elements from a provider's profile, in order to more effectively match patients with providers that are: 1. More compatible with other “like” patients; and 2. Who have the likelihood of producing more favorable outcomes (e.g., satisfaction, adherence, outcomes, preference). Fig.4D and [0077]: the data between patients and physicians may be matched is by disparate data (behavioral) matching, as seen in FIG. 4D. Disparate data matching associates profile attributes from different data sources, to more effectively validate user-generated data with third party data).
Regarding claim 3, Rice teaches wherein generating the graph data from the preprocessed data comprises generating a text labeled graph that is represented with triples or a property graph (Fig.4A - 4D and [0052]-[0059] & [0077]: the interface 102 may provide the user with an application program interface to receive third party data feeds which may be downloaded and stored in the database 108 in association with the patient and/or provider profile account. This downloaded data may be parsed, tokenized and/or analyzed to supplement the questionnaire response data according to a category and/or attribute assigned to each data received from, for example, a social media account and/or medical data database. To access the API, the user may provide authentication information in order to access, download and/or store the user's data in database 108. In this way, patients may append their profiles by connecting their social media profiles or electronic health records to their patient or provider profile. Data from these third party providers may auto-populate select profile questions/categories);
Regarding claims 5 and 14, Rice teaches wherein the at least one processor is further configured to execute the instructions to: obtain medical images of the patient that arrives at the hospital; and convert the medical images to a text as the clinical initial information ([0049]-[0063]: the API may receive third party data feeds in the form of patient data from a medical data database. The disclosed system may analyze and organize the received patient data, identifying data relating to the patient's immediate needs, as well as general medical data, and any additional current or predictive health conditions. In addition to identifying providers that are a match for the patient's current needs, the disclosed system may therefore also analyze the user's request for a matching provider in the context of these additional health conditions, and provide recommendations according to a network of preferred providers within a network associated with each of the recommended providers).
Regarding claims 6 and 15, Rice teaches wherein the clinical initial information of the patient is at least one of a symptom and an initial assumption about possible diseases ([0042]: experience treating a particular medical condition (e.g., atrial fibrillation, bradycardia, tachycardia, etc.), experience treating particular symptoms).
Regarding claims 7 and 16, Rice teaches wherein: the neural network is optimized as to converge local parameters and global parameters, the local parameters are vectors of the clinical narratives, the historical profiles of the patients and the profiles of the doctors, and the global parameters are neural network layers ([0010], [0026] and [0081: applying machine learning algorithms to clinical narratives, patient profiles, and physician profiles, and iteratively adjusting weighted factors to improve matching outcomes]).
Regarding claims 8 and 17, Rice teaches wherein the text preprocessing pipeline is configured to tokenize and remove special characters and punctuations from the obtained data ([0059]: downloaded data is parsed, tokenized, and analyzed prior to use in physician matching).
Regarding claims 9 and 18, Rice teaches wherein the preprocessing includes dictionary construction via term frequency-inverse document frequency (TFIDF) filtering and image tagging ([0059 and [0070]-[0087]: analyzing and categorizing textual data into attribute based data records for matching and recommendation, as well ass associating image data with physician profiles for presentation and matching purposes).
Regarding claims 10 and 19, Rice teaches wherein allocating the doctor to the patient that arrives at the hospital by using the neural network is based on computing a similarity table indicating similarity scores between the patient that arrives at the hospital and the doctors from the profiles of doctors (Fig.5B and [0080]-[0084]: calculating match confidence percentages between patient attributes and physician attributes and ranking physicians based on such similarity scores).
Allowable Subject Matter
Claims 4 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 4 and 13 identify the distinct features “wherein: the obtained data further includes profiles of rooms, wherein the rooms are diagnosis rooms or treatment rooms, the at least one processor is further configured to execute the instructions to: generate graph data from the preprocessed data, wherein: each node of the graph data is a patient, a doctor or a room, and each edge of the graph data is a link between a patient and a doctor, or a link between a patient and a room; allocate the doctor and a room from the profiles of rooms to the patient that arrives at the hospital by using the neural network and the generated graph data, wherein the neural network is machine-learned by further using the profiles of rooms; and output the information indicating the allocated doctor and the allocated room", which are not taught or suggested by the prior art of records.
Claims 4 and 13 would be allowable over the prior art of record because the claimed features as mentioned above in combination with other claimed features are not recited or suggested by the prior art of records.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aydelotte et al. (Pub. No.: US 2020/0013497) “METHOD AND SYSTEM TO FACILITATE PATIENT CARE”
Considered for teachings related to displaying to a physician at least one of: (a)(i) a hospital floor map, (a)(ii) a first patient identifier, and (a)(iii) a second patient identifier, wherein (b)(i) the map displays first and second rooms of the hospital floor, (b)(ii) a first patient is assigned to the first room and the first patient identifier, (b)(iii) a second patient is assigned to the second room and a second patient identifier, and (b)(iv) a first nurse is assigned to the first room and the first patient identifier, (b)(v) and a second nurse is assigned to the second room and the second patient identifier; receiving a selection from the physician, the selection comprising at least one of the displayed first room and the displayed first patient identifier; and sending a communication from the physician to the first nurse, and not the second nurse, in response to receiving the selection.
Does not disclose or suggest wherein: the obtained data further includes profiles of rooms, wherein the rooms are diagnosis rooms or treatment rooms, the at least one processor is further configured to execute the instructions to: generate graph data from the preprocessed data, wherein: each node of the graph data is a patient, a doctor or a room, and each edge of the graph data is a link between a patient and a doctor, or a link between a patient and a room; allocate the doctor and a room from the profiles of rooms to the patient that arrives at the hospital by using the neural network and the generated graph data, wherein the neural network is machine-learned by further using the profiles of rooms; and output the information indicating the allocated doctor and the allocated room.
Omaboe (Pub. No.: US 2005/0108052) “Proces For Diagnosic System And Method Applying Artificial Intelligence Techniques To A Patient Medical Record And That Combines Customer Relationship Management (CRM) And Enterprise Resource Planning (ERP) Software In A Revolutionary Way To Provide A Unique-and Uniquely Powerful And Easy-to-use-tool To Manage Veterinary Or Human Medical Clinics And Hospitals”
Considered for teachings related to generally to the field of medicine and more specifically to a process for diagnostic system and method applying artificial intelligence techniques to a patent medical record and that combines customer relationship management (CRM) and enterprise resource planning (ERP) in a revolutionary way to provide unique and uniquely powerful and easy-to-use-tool to manage veterinary or human medical clinics and hospitals.
Does not disclose or suggest at least one processor configured to access the at least one memory and execute the instructions to: obtain data that includes profiles of doctors and clinical initial information of a patient that arrives at a hospital; preprocess the obtained data, wherein preprocessing includes a text preprocessing pipeline and word embedding; allocate a doctor to the patient that arrives at the hospital by using a neural network, wherein the neural network is machine-learned by using graph data of clinical narratives composed by doctors about patients, historical profiles of the patients and the profiles of doctors; and output information indicating the allocated doctor.
Any inquiry concerning this communication should be directed to Yong Choe at telephone number 571-270-1053 or email to yong.choe@uspto.gov. The examiner can normally be reached on M-F 10:00 am to 6:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutz, Jared Ian can be reached on (571) 272-5535. Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 whose telephone number is (571) 272-2100.
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/YONG J CHOE/Primary Examiner, Art Unit 2135