DETAILED ACTION
The present office action represents a nonfinal action on the merits.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/28/2026 has been entered.
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 .
Priority
This application claims the priority date of September 22, 2022.
Status of Claims
Claims 1, 9-10, 15, and 19-23 are amended and claims 1-11, 14-17, and 19-23 are pending.
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 1-11, 14-17, and 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-11 and 14 are drawn to a method of integrating and training a machine learning computing system communicatively coupled to an electronic medical record (EMR) system into a healthcare system, which is within the four statutory categories (i.e., process). Claims 15-17 are drawn to one or more instances of computer-readable media collectively having contents configured to cause a machine learning computing system communicatively coupled to an electronic medical record (EMR) system integrated into a healthcare system to perform a method, which is within the four statutory categories (i.e., process). Claims 19-23 are drawn to a machine learning computing system communicatively coupled to an electronic medical record (EMR) system integrated into a healthcare system, which is within the four statutory categories (i.e., process).
Claim 1 recites a method of integrating and training a machine learning computing system communicatively coupled to an electronic medical record (EMR) system into a healthcare system, comprising:
(i) receiving first natural language input from a user;
(ii) applying to the first natural language input a machine learning model trained to discern in the first natural language input an intent to refill a prescription;
(iii) generating a first natural language output to the user acknowledging the intent to refill the prescription;
(iv) receiving a second natural language input from the user;
(v) applying to the second natural language input a machine learning model trained to discern in the second natural language input a named entity corresponding to the prescription;
(vi) generating a second natural language output to the user acknowledging the named entity corresponding to the prescription; and
(vii) placing a refill order with a pharmacy by performing at least one of:
generating computer-simulated touch-tones or computer-generated voice using the machine learning model, based at least partly on the intent to refill and the named entity, to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech using the machine learning model, based at least partly on the intent to refill and the named entity, to communicate the refill order via a live ordering telephone line of the pharmacy,
wherein the placing the refill order including acts (i) through (vii) comprises one or more
Claim 15 recites one or more instances of computer-readable media collectively having contents configured to cause a machine learning computing system communicatively coupled to an electronic medical record (EMR) system integrated into a healthcare system to perform a method, the method comprising:
(i)conducting a conversational exchange with a person;
(ii)feeding at least part of the exchange into one or more machine learning models to obtain (1) an intent of refilling a prescription, and (2) an identification of the prescription to be fulfilled; and
(iii)placing a refill order on behalf of the person for the identified prescription, by performing at least one of:
generating computer-simulated touch-tones or computer-generated voice, based at least partly on the intent of refilling the prescription and the identification of the prescription to be fulfilled, to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech, based at least partly on the intent of refilling the prescription and the identification of the prescription to be fulfilled, to communicate the refill order via a live ordering telephone line of the pharmacy,
wherein the placing the refill order including acts (i) through (iii) comprises one or more sequence of the acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined to train the machine learning model.
Claim 19 recites a machine learning computing system communicatively coupled to an electronic medical record (EMR) system integrated into a healthcare system, comprising:
one or more processors; and
memory storing contents that, when executed by the one or more processors, cause the computing system to perform actions integrated into the healthcare system comprising:
(i)receiving a first natural language input from a user;
(ii)applying to the first natural language input a machine learning model trained to discern in the first natural language input an intent to refill a prescription;
(iii)generating a first natural language output to the user acknowledging the intent to refill the prescription;
(iv)receiving a second natural language input from the user;
(v)applying to the second natural language input a machine learning model trained to discern in the second natural language input a named entity corresponding to the prescription;
(vi)generating a second natural language output to the user acknowledging the named entity corresponding to the prescription; and
(vii)placing a refill order with a pharmacy by performing at least one of:
generating computer-simulated touch-tones or computer-generated voice based, at least partly on the intent to refill and the named entity to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech based, at least partly, on the intent to refill and the named entity to communicate the refill order via a live ordering telephone line of the pharmacy,
wherein the placing the refill order including acts (i) through (vii) comprises one or more sequence of the acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined to train the machine learning computing system.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites limitations that are managing personal behavior or relationships or interactions between people (e.g., obtaining patient prescription information) and mathematical concepts. The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or mathematical concepts) and are deemed “additional elements,” and will be discussed in further detail below. If a claim limitation, under its broadest reasonable interpretation, is managing personal behavior or interactions between people but for the recitation of generic computer components, then it fails within the “method of organizing human activity” or “mathematical concept” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims 2-11, 14, 16-17, and 20-23 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The dependent claims recite additional limitations but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 15, and 19.
The additional elements from claims 1, 15, and 19 include:
a machine learning computing system (apply it, MPEP 2106.05(f)).
an electronic medical record (EMR) system (apply it, MPEP 2106.05(f)).
The additional elements from claim 9 include:
retrieve from an electronic medical record program (apply it, MPEP 2106.05(f)).
The additional elements from claim 14 include:
a telephone response system (apply it, MPEP 2106.05(f)).
The additional elements from claim 15 include:
one or more instances of computer-readable media (apply it, MPEP 2106.05(f)).
The additional elements from claim 16 include:
an audio input device (apply it, MPEP 2106.05(f)).
a text-to-speech mechanism (apply it, MPEP 2106.05(f)).
an audio output device (apply it, MPEP 2106.05(f)).
The additional elements from claim 17 include:
a text input device (apply it, MPEP 2106.05(f)).
a visual display device (apply it, MPEP 2106.05(f)).
The additional elements from claim 19 include:
one or more processors (apply it, MPEP 2106.05(f)).
memory storing contents that, when executed by the one or more processors, cause the system to perform actions (apply it, MPEP 2106.05(f)).
The dependent claims include other limitations including
a deep bidirectional transformer for language understanding (apply it, MPEP 2106.05(f)).
Furthermore, claims 1-11, 14-17, and 19-23 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “computing system”, “electronic medical record program”, “telephone response system”, “computer readable media”, “device”, “memory”, “processor”, and “display”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see, Specification Pages, 5-7. (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Specification Pages 5-7 disclose that the additional elements (i.e., computer, system, program) comprise a plurality of different types of generic computing systems that are configured to perform generic computer that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., patient prescription refill requests).
Dependent claims 2-11, 14, 16-17, and 20-23 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves patient refill requests or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1-11, 14-17, and 19-23 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
Claims 1-2, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky (U.S. Pub. No. 2022/0012430 A1) in view of Bostic (U.S. Pub. No. 20210225521 A1), Varga (U.S. Pub. No. 2022/0101860 A1), and DiVenuta (U.S. Pub. No. 2005/0069103 A1).
Regarding claim 1, Kanefsky discloses a method of integrating and training a machine learning computing system communicatively coupled to a system into a healthcare system, comprising (Paragraphs [0002], [0010], [00032]-[0033], [0037], and Table 1 discuss a system and method, a computing system, a pharmacy system, and a computer implemented pharmacy order facilitation method, the server is a component of the system and the server includes a ML training module, and the ML operation module may act in concert to retrieve labeled data, to train and/or receive machine learning models, and to use the trained machine learning models to produce valuable outputs, the server may be communicatively coupled to one or more electronic databases that store training data, intents data, and customer data.):
(i)receiving first natural language input from a user (Paragraphs [0002] and [0009] discuss facilitating pharmacy customer orders through natural language processing of messages and receiving a message of a pharmacy customer.);
(ii)applying to the first natural language input a machine learning model trained to discern in the first natural language input an intent to refill a prescription (Paragraphs [0036], [0041]-[0042], [0045]-[0048], and Table 1 discuss ML operation module may load the trained ML model and execute the trained ML model using input data (i.e., apply the input data to the trained ML model) from a real-world scenario (e.g., a message texted to the server by a customer), the ML model determines the intent of the customer to refill the medication.);
(iii)generating a first natural language output to the user acknowledging the intent to refill the prescription (Claims 4 and 9 discuss generating the response message incudes generating a confirmation based on an intent of the customer to refill.);
(iv)receiving a second natural language input from the user (Paragraphs [0005], [0023], Table US 00001 discuss receive a message from a customer including, e.g., “refill”, “status”, or “please cancel”, indicating the customer intent.);
(v)applying to the second natural language input a machine learning model trained to discern in the second natural language input a named entity corresponding to the prescription (Paragraphs [0041], [0061], Table 00001 discuss the ML operation module may select an appropriate trained model at the time the ML operation module receives the cleaned message, for example, on January 1, a prescription refill system may send a refill reminder to a customer via SMS, “It looks like your budesonide inhaler, 90 mcg/actuation is due for refill. Do you wish to refill your prescription?” On January 3, the customer may reply with an inbound message stating that, “Hello, yes I would like to refill that.” After analysis by the ML model determines that the message is of type RefillRequest, the ML operation module may transmit a message such as “(RefillRequest: Yes, PatientID: XYZ123)” to the prescription refill system.);
(vi)generating a second natural language output to the user acknowledging the named entity corresponding to the prescription (Paragraphs [0057]-[0058], FIGS. 2, 3A, and Table 00001 discuss the system outputs a response to customer’s input, for example, the customer states, “Already picked” and the system will generate a response that apologizes to the customer, and thanks the customer for his business.); and
(vii)placing a refill order with a pharmacy by performing at least one of (Paragraph [0062], Table 00001 discuss once the prescription refill system locates the record of the patient based on the patient ID, the prescription refill system may take additional actions, such as causing a prescription fill order to be transmitted to a default local pharmacy of the customer, or a prescription mailing service to generate a postal mail order to the customer, where the refill is included in the mailing.), or
wherein the placing the refill order including acts (i) through (vii) comprises one or more
Kanefsky does not explicitly disclose:
communicatively coupled to an electronic medical record (EMR) system;
generating computer-simulated touch-tones or computer-generated voice using the machine learning model, based at least party on the intent to refill and the named entity, to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech using the machine learning model, based at least partly on the intent to refill and the named entity, to communicate the refill order via a live ordering telephone line of the pharmacy; and
comprises one or more
Bostic teaches:
communicatively coupled to an electronic medical record (EMR) system (Paragraphs [0022] and [0046] discuss system includes using training data from a prescription medication data set in communication with a healthcare database and configured to receive demographic records, diagnosis records, prescription records, and testing records from a plurality of healthcare databases, from a plurality of healthcare providers.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, communicatively coupled to an electronic medical record (EMR) system, as taught by Bostic, in order to provide improved methods and systems for detecting and addressing situations involving improper prescription of medication, improper utilization of prescribed medications, and diversion of prescribed medications to unprescribed uses. (Bostic Paragraph [0005]).
Varga teaches:
generating computer-simulated touch-tones or computer-generated voice using the machine learning model (Paragraphs [0021]-[0022] discuss combined datasets that can be stored in a repository (i.e., knowledge base) and correlated to create training data to train a predetermined machine learning model. The training data can be analyzed to identify a correlation among spoken tone associated with a contextual situation based on skills of a user and how surrounding influencing factors change the spoken tone and emotion of the user, and based on the correlation, a robot can then generate speech from the model.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, generating computer-simulated touch-tones or computer-generated voice using the machine learning model, as taught by Varga, in order to allow robotic systems to reproduce human-like speech that matches a surrounding context of a user and user's persona. (Varga Paragraph [0019]).
DiVenuta teaches:
generating computer-simulated touch-tones or computer-generated voice, based at least party on the intent to refill and the named entity, to communicate the refill order via a voice-response telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.), or
generating computer-generated speech, based at least partly on the intent to refill and the named entity, to communicate the refill order via a live ordering telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.);
comprises one or more order of acts selected from the group consisting of : rearranged and performed in parallel (Examiner is interpreting this limitation pursuant to Applicant’s FIG. 3 and Specification page 9, for example, the order of the acts may be rearranged; some acts may be performed in parallel; shown acts may be omitted, or other acts may be included; a shown act may be divided into subacts, or multiple shown acts may be combined into a single act, etc.) (Paragraphs [0024], [0031]-[0033], and FIGS. 1-3 discuss refill and targeted messages that are included in the exchange in response to when a response is received from caller to a targeted message to refill a prescription, various targeted messages are used depending on response, the functions/acts steps for refilling a prescription noted in the blocks may occur out of the order noted in the flowcharts, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include generating computer-simulated touch-tones or computer-generated voice, based at least party on the intent to refill and the named entity, to communicate the refill order via a voice-response telephone line of the pharmacy, generating computer-generated speech, based at least partly on the intent to refill and the named entity, to communicate the refill order via a live ordering telephone line of the pharmacy, and comprises one or more order of acts selected from the group consisting of : rearranged and performed in parallel, as taught by DiVenuta, in order to allow callers to perform various transactions without the need for direct assistance from a customer service representative or associate. (DiVenuta Paragraph [0002]).
Regarding claim 2, Kanefsky discloses wherein machine learning model for the first natural language input and the machine learning model for the second natural input are the same machine learning language model (Paragraphs [0035]-[0042], [0045]-[0048], [0061], and Table 00001 discuss training machine learning models and the system may include instructions for analyzing the telephone number associated with the cleaned message to identify a customer, and for retrieving one or more ML models associated with that customer and the ML operation module may initialize the trained model and apply the cleaned message to the trained, initialized ML model, which determines customer intent and the prescription to refill.).
Regarding claim 6, Kanefsky discloses further comprising, after receiving the first natural language input from the user:
using identifying information for the user to retrieve from an electronic medical record a list of prescriptions written for the user (Paragraphs [0038], [0060]-[00662] discuss the prescription refill system locates the record of the patient based on the patient ID and patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
providing third natural language output to the user identifying each of at least a portion of the prescriptions on the list of prescriptions written for the user (Paragraphs [0038], [0060]-[0062] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the prescription refill system may send a refill reminder outbound message to a pharmacy customer and other information may be included such as the status as a controlled substance, precautions, etc.).
Regarding claim 8, Kanefsky discloses further comprising, after receiving the first natural language input from the user:
using identifying information for the user to retrieve from an electronic medical record, for each of a plurality of prescriptions written for the user, a drug name specified by the prescription (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
providing third natural language output to the user identifying each of at least a portion of the retrieved drug names (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the prescription refill system may send a refill reminder outbound message to a pharmacy customer and other information may be included such as the status as a controlled substance, precautions, etc.).
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic, Varga, and DiVenuta and in further view of Lebanoff (U.S. Pub. No. 2022/0261555 A1).
Regarding claim 3, Kanefsky discloses wherein the machine learning model is a neural network for language understanding (Paragraphs [0042]-[0043] discusses the machine learning model and multiple different types of artificial neural networks may be employed, including, recurrent neural networks, convolutional neural networks, and/or deep learning neural networks to determine customer intent from a message.).
Kanefsky does not explicitly disclose:
a deep bidirectional transformer
Lebanoff teaches:
a deep bidirectional transformer (Paragraphs [0100]-[0102] discuss bidirectional encoder representations from transformers is used as a language representation model and the model uses a deep bidirectional transformer.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include a deep bidirectional transformer, as taught by Lebanoff, in order to predict a sentence that combines information from a first sentence and a second sentence in a natural language processing application. (Lebanoff Paragraph [0005]).
Regarding claim 4, Kanefsky discloses wherein the machine learning model is a domain-invariant (Examiner is interpreting “domain-invariant” to mean that features remain consistent across different domains or datasets.) neural network for language understanding model (Paragraphs [0042]-[0043], and [0048] discuss the machine learning model and multiple different types of artificial neural networks may be employed, including, recurrent neural networks, convolutional neural networks, and/or deep learning neural networks to determine customer intent from a message and a set of frequent intents and class association rules may be used to train the machine learning model.).
Kanefsky does not explicitly disclose:
a domain-invariant learning with bidirectional transformer
Lebanoff teaches:
a domain-invariant learning with bidirectional transformer (Paragraphs [0100]-[0102] discuss bidirectional encoder representations from transformers is used as a language representation model and the model uses a deep bidirectional transformer.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include a domain-invariant learning with bidirectional transformer, as taught by Lebanoff, in order to predict a sentence that combines information from a first sentence and a second sentence in a natural language processing application. (Lebanoff Paragraph [0005]).
Regarding claim 5, Kanefsky discloses wherein the machine learning model is a dual intent and entity transformer (Paragraphs [0042]-[0043] discusses the machine learning model and multiple different types of artificial neural networks may be employed, including, recurrent neural networks, convolutional neural networks, and/or deep learning neural networks to determine customer intent from a message.).
Kanefsky does not explicitly disclose:
a dual intent and entity transformer
Lebanoff teaches:
a dual intent and entity transformer (Paragraphs [0006] discuss a coreference model configured to generate coreference information for a first sentence and a second sentence, wherein the coreference information identifies entities associated with both a term of the first sentence and a term of the second sentence and a sentence fusion network comprising a transformer model wherein at least one attention head of the transformer model is constrained by an entity constraint.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include a dual intent and entity transformer, as taught by Lebanoff, in order to predict a sentence that combines information from a first sentence and a second sentence in a natural language processing application. (Lebanoff Paragraph [0005]).
Claims 7, 9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic, Varga, and DiVenuta and in further view of Carr (U.S. Pub. No. 2023/0031651 A1).
Regarding claim 7, Kanefsky discloses wherein the discerned named entity corresponds to a drug (Paragraph [0061] discusses the prescription refill system sends a refill reminder outbound message, for example, “It looks like your budesonide inhaler, 90 mcg/actuation is due for refill.”).
Kanefsky does not explicitly disclose:
the discerned named entity.
Carr teaches the discerned named entity (Paragraphs [0034] and [0036] discuss various entities (e.g., users, professionals, medical professionals, callers, patients) and the communication process can monitor conversations between various entities so that for example, professionals may be monitored and assistance may be rendered.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include the discerned named entity, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Regarding claim 9, Kanefsky discloses further comprising, after receiving the second natural language input from the user:
using identifying information for the user and for the prescription to which someone corresponds to retrieve from an electronic medical record program a record for the prescription to which someone (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
determining from the retrieved record whether the prescription to which someone corresponds is in condition to be refilled, and wherein the placing the refill order is performed in response to determining that the prescription to which someone corresponds is in condition to be refilled (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions, for example, on January 1, a prescription refill system may send a refill reminder outbound message to a customer, the message may read, “It looks like your budesonide inhaler, 90 mcg/actuation is due for refill. Do you wish to refill your prescription?” On January 3, the customer may reply with an inbound message stating that, “Hello, yes I would like to refill that.”.).
Kanefsky does not explicitly disclose:
the discerned named entity.
Carr teaches:
the discerned named entity (Paragraphs [0034] and [0036] discuss various entities (e.g., users, professionals, medical professionals, callers, patients) and the communication process can monitor conversations between various entities so that for example, professionals may be monitored and assistance may be rendered.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include the discerned named entity, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Regarding claim 14, Kanefsky discloses further comprising placing a message to a response system provided by the pharmacy (Paragraphs [0022]-[0023], [0031] and Table 00001 discuss the interface and the mobile device allows the user to receives and sends messages (messages include text, email, voice mail, audio, and after receiving from the customer “Yes, please refill: Initiate a refill for the medication”, the pharmacy system refills the order.).
Kanefsky does not explicitly disclose:
placing a telephone call to a telephone response system.
Carr teaches:
placing a telephone call to a telephone response system (Paragraphs [0081]-[0082] discuss the communication process monitors a conversation and calling to request medication.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include placing a telephone call to a telephone response system, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic, Varga, and DiVenuta and in further view of Carr and Gupta (U.S. Pub. No. 2014/0278531 A1).
Regarding claim 10, Kanefsky discloses further comprising, after receiving the second natural language input from the user:
using identifying information for the user and for the prescription to which someone corresponds to retrieve from an electronic medical record a record for the prescription to which someone corresponds (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.);
determining from the retrieved record whether the prescription to which someone corresponds is in condition to be refilled (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
Kanefsky does not explicitly disclose:
the discerned named entity;
in response to determining that the prescription to which the discerned named entity corresponds is not in condition to be refilled, using identifying information in the retrieved record for a physician who prescribed the prescription to which the discerned named entity to obtain authorization from the physician to amend the prescription to which the discerned named entity, and wherein the placing the refill order is performed in response to obtaining the authorization to amend.
Carr teaches:
the discerned named entity (Paragraphs [0034] and [0036] discuss various entities (e.g., users, professionals, medical professionals, callers, patients) and the communication process can monitor conversations between various entities so that for example, professionals may be monitored and assistance may be rendered.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include the discerned named entity, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Gupta teaches:
in response to determining that the prescription to which the discerned named entity corresponds is not in condition to be refilled, using identifying information in the retrieved record for a physician who wrote the prescription to which the discerned named entity corresponds to obtain authorization from the physician to amend the prescription to which the discerned named entity corresponds, and wherein the causing a refill order to be placed is performed in response to obtaining the authorization to amend (Paragraphs [0017]-[0021], [0047] discuss the refill request processing system determines the refill request could not be authorized and routes messages and refill requests to the particular health care providers and solicits input to authorize a refill request if needed.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include in response to determining that the prescription to which the information corresponds is not in condition to be refilled, using identifying information in the retrieved record for a physician who wrote the prescription to which the information corresponds to obtain authorization from the physician to amend the prescription to which the information corresponds, and wherein the causing a refill order to be placed is performed in response to obtaining the authorization to amend, as taught by Gupta, in order to streamline the tasks involved in providing health care to patients and improve the accuracy and the efficiency of the prescription and renewal process. (Gupta Paragraphs [0002]-[0003]).
Regarding claim 11, Kanefsky discloses wherein the placing the refill order comprises sending to someone associated with the prescription to which someone corresponds a notification to order refill of the prescription from the pharmacy (Paragraph [0021] discusses dispatch pharmacy customer refill requests to appropriate staff based on the customer’s intent.).
Kanefsky does not explicitly disclose:
sending to a medical assistant;
the discerned named entity.
Carr teaches:
the discerned named entity (Paragraphs [0034] and [0036] discuss various entities (e.g., users, professionals, medical professionals, callers, patients) and the communication process can monitor conversations between various entities so that for example, professionals may be monitored and assistance may be rendered.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include the discerned named entity, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Gupta teaches:
sending to a medical assistant (Paragraph [0047] discusses the refill request processing system routes messages and refill requests to a nurse or assistant.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include sending to a medical assistant, as taught by Gupta, in order to streamline the tasks involved in providing health care to patients and improve the accuracy and the efficiency of the prescription and renewal process. (Gupta Paragraphs [0002]-[0003]).
Claims 15, 17, 19-20, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic and DiVenuta.
Regarding claim 15, Kanefsky discloses one or more instances of computer-readable media collectively having contents configured to cause machine learning computing system communicatively coupled to a system integrated into a healthcare system to perform a method, the method comprising (Paragraphs [0002], [0010], [00032]-[0033], [0037], and Table 1 discuss a system and method, a computing system, a pharmacy system, and a computer implemented pharmacy order facilitation method, the server is a component of the system and the server includes a ML training module, and the ML operation module may act in concert to retrieve labeled data, to train and/or receive machine learning models, and to use the trained machine learning models to produce valuable outputs, the server may be communicatively coupled to one or more electronic databases that store training data, intents data, and customer data.):
(i)conducting a conversational exchange with a person (Paragraphs [0005], [0023], Table US 00001 discuss facilitate a pharmacy customer order the customer may send and receive messages from pharmacy, including, e.g., “refill”, “status”, or “please cancel”, indicating the customer intent.);
(ii)feeding at least part of the exchange into one or more machine learning models to obtain (1) an intent of refilling a prescription, and (2) an identification of the prescription to be fulfilled (Paragraphs [0041] and [0061] discuss the machine language operation module may select an appropriate trained model when it receives a message and the prescription refill system determines intent to refill a prescription, for example system may send refill reminder outbound message to pharmacy customer, “It looks like your budesonide inhaler, 90 mcg/actuation is due for refill. Do you wish to refill your prescription?” and customer may reply, “Hello, yes I would like to refill that.”, after analysis the machine learning model determines that the message is of type RefillRequest.); and
(iii)placing a refill order on behalf of the person for the identified prescription, by performing at least one of (Paragraphs [0061]-[0062] discuss once the prescription refill system locates the record of the patient, it causes the order to be transmitted to a local pharmacy or prescription mailing service to generate the order to the customer.),
wherein the placing the refill order including acts (i) through (iii) comprises one or more sequence of the acts combined to train the machine learning model (Paragraphs [0009], [0031]-[0033], and FIG. 3 discuss refill and targeted messages that are included in the exchange in response to when a response is received from caller to a targeted message to refill a prescription, various targeted messages are used depending on response; a computer-implemented pharmacy order facilitation method includes (i) receiving a message of a pharmacy customer; (ii) training a machine learning model using a set of messages each having a respective intent label specifying a pharmacy customer objective; (iii) generating, by analyzing the message using the trained machine learning model, a set of one or more intents of the pharmacy customer; (iv) generating a response message based on the set of intents; and (v) transmitting the response message to a mobile device of the pharmacy customer.).
Kanefsky does not explicitly disclose:
communicatively coupled to an electronic medical record (EMR) system;
generating computer-simulated touch-tones or computer-generated voice, based at least partly on the intent of refilling the prescription and the identification of the prescription to be fulfilled, to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech, based at least partly on the intent of refilling the prescription and the identification of the prescription to be fulfilled, to communicate the refill order via a live ordering telephone line of the pharmacy; and
comprises one or more sequence of the acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided and combined.
Bostic teaches:
communicatively coupled to an electronic medical record (EMR) system (Paragraphs [0022] and [0046] discuss system includes using training data from a prescription medication data set in communication with a healthcare database and configured to receive demographic records, diagnosis records, prescription records, and testing records from a plurality of healthcare databases, from a plurality of healthcare providers.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, communicatively coupled to an electronic medical record (EMR) system, as taught by Bostic, in order to provide improved methods and systems for detecting and addressing situations involving improper prescription of medication, improper utilization of prescribed medications, and diversion of prescribed medications to unprescribed uses. (Bostic Paragraph [0005])
DiVenuta teaches:
generating computer-simulated touch-tones or computer-generated voice, based at least partly on the intent of refilling a prescription and the identification of a prescription to be fulfilled, to communicate the refill order via a voice-response telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.), or
generating computer-generated speech, based at least partly on the intent of refilling a prescription and the identification of a prescription to be fulfilled, to communicate the refill order via a live ordering telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.); and
comprises one or more sequence of the acts selected from the group consisting of : rearranged and performed in parallel (Paragraphs [0024], [0031]-[0033], and FIGS. 1-3 discuss refill and targeted messages that are included in the exchange in response to when a response is received from caller to a targeted message to refill a prescription, various targeted messages are used depending on response, the functions/acts steps for refilling a prescription noted in the blocks may occur out of the order noted in the flowcharts, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, generating computer-simulated touch-tones or computer-generated voice, based at least partly on the intent of refilling a prescription and the identification of a prescription to be fulfilled, to communicate the refill order via a voice-response telephone line of the pharmacy, generating computer-generated speech, based at least partly on the intent of refilling a prescription and the identification of a prescription to be fulfilled, to communicate the refill order via a live ordering telephone line of the pharmacy, and comprises one or more sequence of the acts selected from the group consisting of : rearranged and performed in parallel, as taught by DiVenuta, in order to allow callers to perform various transactions without the need for direct assistance from a customer service representative or associate. (DiVenuta Paragraph [0002]).
Regarding claim 17, Kanefsky discloses wherein the conversational exchange is a free-text exchange in which the person's contributions are received via a text input device, and in which the computing system's contributions are outputted by a visual display device (Paragraphs [0022]-[0024], [0031] and Table 00001 discuss the interface and the mobile device allows the user to receives and sends messages (messages include text, email, voice mail, audio, and after receiving from the customer “Yes, please refill: Initiate a refill for the medication”, the pharmacy system refills the order, the output and input may be communicated with a mobile device, personal computer, smart phone, tablet, etc.).
Regarding claim 19, Kanefsky discloses a machine learning computing system communicatively coupled to a system integrated into a healthcare system, comprising (Paragraphs [0002] and [0010] discuss a system and method, a computing system, and a computer implemented pharmacy order facilitation method.):
one or more processors (Paragraphs [0022]-[0024] discuss a system with any number of processors.); and
memory storing contents that, when executed by the one or more processors, cause the computing system to perform actions integrated into the healthcare system comprising (Paragraphs [0011] and [0022] discusses storage memories containing computer readable instructions for execution by a processor of the system, the computing system perform an action with respect to pharmacy order corresponding to a customer.):
(i)receiving a first natural language input from a user (Paragraph [0023] discusses the system may receive a message from a user that comprises textual or text-like data, such as a text/SMS message, an email, a voice mail, an audio recording, a tweet, etc.);
(ii)applying to the first natural language input a machine learning model trained to discern in the first natural language input an intent to refill a prescription (Paragraphs [0036], [0041]-[0042], [0045]-[0048], and Table 1 discuss ML operation module may load the trained ML model and execute the trained ML model using input data (i.e., apply the input data to the trained ML model) from a real-world scenario (e.g., a message texted to the server by a customer), the ML model determines the intent of the customer to refill the medication.);
(iii)generating a first natural language output to the user acknowledging the intent to refill the prescription (Claims 4 and 9 discuss generating the response message incudes generating a confirmation based on an intent of the customer to refill.);
(iv)receiving a second natural language input from the user (Paragraphs [0005], [0023], Table US 00001 discuss receive a message from a customer including, e.g., “refill”, “status”, or “please cancel”, indicating the customer intent.);
(v)applying to the second natural language input a machine learning model trained to discern in the second natural language input a named entity corresponding to the prescription (Paragraphs [0041], [0061], Table 00001 discuss the ML operation module may select an appropriate trained model at the time the ML operation module receives the cleaned message, for example, on January 1, a prescription refill system may send a refill reminder to a customer via SMS, “It looks like your budesonide inhaler, 90 mcg/actuation is due for refill. Do you wish to refill your prescription?” On January 3, the customer may reply with an inbound message stating that, “Hello, yes I would like to refill that.” After analysis by the ML model determines that the message is of type RefillRequest, the ML operation module may transmit a message such as “(RefillRequest: Yes, PatientID: XYZ123)” to the prescription refill system.);
(vi)generating a second natural language output to the user acknowledging the named entity corresponding to the prescription (Paragraphs [0057]-[0058], FIGS. 2, 3A, and Table 00001 discuss the system outputs a response to customer’s input, for example, the customer states, “Already picked” and the system will generate a response that apologizes to the customer, and thanks the customer for his business.); and
(vii)placing a refill order with a pharmacy by performing at least one of (Paragraph [0062], Table 00001 discuss once the prescription refill system locates the record of the patient based on the patient ID, the prescription refill system may take additional actions, such as causing a prescription fill order to be transmitted to a default local pharmacy of the customer, or a prescription mailing service to generate a postal mail order to the customer, where the refill is included in the mailing.),
wherein the placing the refill order including acts (i) through (vii) comprises one or more sequence of the acts combined to train the machine learning computing system (Paragraphs [0009], [0031]-[0033], and FIG. 3 discuss refill and targeted messages that are included in the exchange in response to when a response is received from caller to a targeted message to refill a prescription, various targeted messages are used depending on response; a computer-implemented pharmacy order facilitation method includes (i) receiving a message of a pharmacy customer; (ii) training a machine learning model using a set of messages each having a respective intent label specifying a pharmacy customer objective; (iii) generating, by analyzing the message using the trained machine learning model, a set of one or more intents of the pharmacy customer; (iv) generating a response message based on the set of intents; and (v) transmitting the response message to a mobile device of the pharmacy customer.).
Kanefsky does not explicitly disclose:
communicatively coupled to an electronic medical record (EMR) system;
generating computer-simulated touch-tones or computer-generated voice based, at least partly on the intent to refill and the named entity to communicate the refill order via a voice-response telephone line of the pharmacy, or
generating computer-generated speech based, at least partly, on the intent to refill and the named entity to communicate the refill order via a live ordering telephone line of the pharmacy; and
comprises one or more sequence of the acts selected from the group consisting of: rearranged, performed in parallel, omitted, included, divided.
Bostic teaches:
communicatively coupled to an electronic medical record (EMR) system (Paragraphs [0022] and [0046] discuss system includes using training data from a prescription medication data set in communication with a healthcare database and configured to receive demographic records, diagnosis records, prescription records, and testing records from a plurality of healthcare databases, from a plurality of healthcare providers.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, communicatively coupled to an electronic medical record (EMR) system, as taught by Bostic, in order to provide improved methods and systems for detecting and addressing situations involving improper prescription of medication, improper utilization of prescribed medications, and diversion of prescribed medications to unprescribed uses. (Bostic Paragraph [0005]).
DiVenuta teaches:
generating computer-simulated touch-tones or computer-generated voice based, at least partly on the intent to refill and the named entity to communicate the refill order via a voice-response telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.), or
generating computer-generated speech based, at least partly, on the intent to refill and the named entity to communicate the refill order via a live ordering telephone line of the pharmacy (Paragraphs [0025]-[0026] and [0037] discuss a pharmacy interactive voice response system that allows callers to interact with a phone system through voice input to refill a prescription with a targeted message module that can include prerecorded voice prompts or text-to-speech generated segments.); and
comprises one or more sequence of the acts selected from the group consisting of : rearranged and performed in parallel (Paragraphs [0024], [0031]-[0033], and FIGS. 1-3 discuss refill and targeted messages that are included in the exchange in response to when a response is received from caller to a targeted message to refill a prescription, various targeted messages are used depending on response, the functions/acts steps for refilling a prescription noted in the blocks may occur out of the order noted in the flowcharts, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.)
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include, generating computer-simulated touch-tones or computer-generated voice based, at least partly on the intent to refill and the named entity to communicate the refill order via a voice-response telephone line of the pharmacy, generating computer-generated speech based, at least partly, on the intent to refill and the named entity to communicate the refill order via a live ordering telephone line of the pharmacy, and comprises one or more sequence of the acts selected from the group consisting of : rearranged and performed in parallel, as taught by DiVenuta, in order to allow callers to perform various transactions without the need for direct assistance from a customer service representative or associate. (DiVenuta Paragraph [0002]).
Regarding claim 20, Kanefsky discloses wherein the machine learning model for the first natural language input and the machine learning model for the second natural language input are a same machine learning language model (Paragraphs [0035]-[0042], [0045]-[0048], [0061], and Table 00001 discuss training machine learning models and the system may include instructions for analyzing the telephone number associated with the cleaned message to identify a customer, and for retrieving one or more ML models associated with that customer and the ML operation module may initialize the trained model and apply the cleaned message to the trained, initialized ML model, which determines customer intent and the prescription to refill.).
Regarding claim 22, Kanefsky discloses wherein the memory storing further contents that, when executed by the one or more processors, cause the computing system to perform further actions comprising (Paragraphs [0022]-[0024] discuss a system with any number of processors and modules may be implemented as computer-readable storage memories containing computer readable instructions (i.e., software) for execution by a processor of the system.):
using identifying information for the user to retrieve from an electronic medical record a list of prescriptions written for the user (Paragraphs [0038], [0060]-[00662] discuss the prescription refill system locates the record of the patient based on the patient ID and patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
providing third natural language output to the user identifying each of at least a portion of the prescriptions on the retrieved list (Paragraphs [0038], [0060]-[0062] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the prescription refill system may send a refill reminder outbound message to a pharmacy customer and other information may be included such as the status as a controlled substance, precautions, etc.).
Regarding claim 23, Kanefsky discloses wherein the memory storing further contents that, when executed by the one or more processors, cause the computing system to perform further actions comprising (Paragraphs [0022]-[0024] discuss a system with any number of processors and modules may be implemented as computer-readable storage memories containing computer readable instructions (i.e., software) for execution by a processor of the system.):
using identifying information for the user to retrieve from an electronic medical record, for each of a plurality of prescriptions written for the user, a drug name specified by the prescription (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the customer data includes prescription status of the customer, e.g., any prescriptions that are currently associated with the customer and the status of those prescriptions.); and
providing third natural language output to the user identifying each of at least a portion of the retrieved drug names (Paragraphs [0038], [0060]-[00662] discuss patient ID may be sent to a refill module, which identifies any pending refill inquiries matching the patient ID and the refill module may initiate a refill if a match is found, the prescription refill system may send a refill reminder outbound message to a pharmacy customer and other information may be included such as the status as a controlled substance, precautions, etc.).
Claim 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic, and DiVenuta and in further view of Carr.
Regarding claim 16, Kanefsky discloses wherein the conversational exchange is a spoken exchange in which the person's contributions are received via an audio input device, and in which the computing system's contributions are generated by a text- mechanism and outputted by an audio output device (Paragraphs [0022]-[0024], [0031] and Table 00001 discuss the interface and the mobile device allows the user to receives and sends messages (messages include text, email, voice mail, audio, and after receiving from the customer “Yes, please refill: Initiate a refill for the medication”, the pharmacy system refills the order, the output and input may be communicated with a mobile device, personal computer, smart phone, tablet, etc.).
Kanefsky does not explicitly disclose:
generated by a text-to-speech mechanism.
Carr teaches:
generated by a text-to-speech mechanism (Paragraph [0136] discusses text-to-speech system converts normal language text into speech.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include generated by a text-to-speech mechanism, as taught by Carr, in order to have a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them and to accurately extract information and insights contained in the documents and categorize and organize information. (Carr Paragraph [0043]).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Kanefsky in view of Bostic and DiVenuta and in further view of Lebanoff.
Regarding claim 21, Kanefsky discloses wherein the machine learning model is a neural network for language understanding (Paragraphs [0042]-[0043] discusses the machine learning model and multiple different types of artificial neural networks may be employed, including, recurrent neural networks, convolutional neural networks, and/or deep learning neural networks to determine customer intent from a message.).
Kanefsky does not explicitly disclose:
a deep bidirectional transformer
Lebanoff teaches:
a deep bidirectional transformer (Paragraphs [0100]-[0102] discuss bidirectional encoder representations from transformers is used as a language representation model and the model uses a deep bidirectional transformer.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Kanefsky to include a deep bidirectional transformer, as taught by Lebanoff, in order to predict a sentence that combines information from a first sentence and a second sentence in a natural language processing application. (Lebanoff Paragraph [0005]).
Response to Arguments
Applicant’s arguments filed 7/28/2026 have been fully considered.
Claim rejections 35 U.S.C. 112(b):
Examiner withdraws the claim rejections in light of Applicant’s amendments.
Claim rejections 35 U.S.C. 101:
With respect to claim 1 and the 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity or mathematical concepts. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant states, “claims 1, 15, and 19 for executing computer programs and/or training or applying machine learning models (as described in paragraphs [0012] and [0019]-[0021] and claims 1, 15, and 19 of the published application) are not directed to an abstract idea, under Step 2a prong 1 of the flowchart provided in MPEP 2106 subsection III, according to the August 2025 Memo that revise the procedures for determining whether a claim is directed to a judicial exception. The August 2025 Memo directs examiners to consider whether software-related arts including Artificial Intelligence (AI) and Machine Learning function may be performed in a human mind before declaring the patent an ineligible mental process.” (Remarks, page 11). Examiner respectfully disagrees. Examiner noted above that claim 1 recites the abstract idea of organizing human activity or mathematical concepts. Mental process is not recited above. Applicant’s arguments are not applicable to the arguments discussed above.
While practical application is a way to overcome the Prong 2 35 U.S.C. 101 rejection, here, claim 1 fails to integrate the recited judicial exception into a practical application. Applicant states, “under Step 2a prong 2 of the flowchart provided in MPEP 2106 subsection III, the August 2025 Memo clarifies that explicit explanation of a resulting technological improvement does not need to be in the specification for an abstract idea to be incorporated into a practical application…the pending claims recite a practical application that is used in conjunction with a particular machine that is integral to the claim and causes "healthcare facility management system" to present the information related to an healthcare plan so that the healthcare facility management system can make an important decision regarding the healthcare related plan.” (Remarks, pages 12-13). Here, a machine learning computing system communicatively coupled to an electronic medical record (EMR) system integrated into a healthcare system for facilitating "healthcare facility management" technology that "the inventors have recognized significant disadvantages in conventional approaches available to patients to request prescription refills, does not result in a practical application as it is recited as part of the abstract idea, as stated above. All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. For the reasons stated above, claims 15 and 19 similarly fail to overcome the 35 U.S.C. 101 rejection.
Rejections under 35 U.S.C. 103:
With respect to claim 1 and the 35 U.S.C. 103 rejection, Applicant’s amendment overcomes the previous rejection. Applicant’s arguments with respect to claims 1, 9, and 15 have been considered and the Examiner’s rejection has been updated to address Applicant’s claim amendments and additions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:30PM.
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/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights