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 .
The present Office Action is in response to the Request for Continued Examination dated 28 January 2026.
Request for Continued Examination
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 28 January 2026 has been entered.
DETAILED ACTION
In the RCE filed 28 January 2026:
Claims 1,12,17-20 are amended
Claims 1-20 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 1-20 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, 17-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite a method, system and non-transitory computer-readable medium, which are within a statutory category.
Step 2A1
The limitations of:
Claims 1, 17-18 (Claim 1 being representative)
receiving a user command comprising natural language, wherein the user command includes personal information, and the user command provides a request to process medical data;
pre-processing the user command to mask the personal information;
transmitting a prompt, the prompt invoking to determine an argument to use based on information provided from the pre-processed user command,
the medical data is not accessible;
receiving a chatbot-determined argument, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled, and wherein the one or more functions, methods, services or components are identified to apply one or more specific actions to input data based on the chatbot-determined argument;
and processing the medical data using the identified programmatic feature including executing to perform the chatbot-determined action or argument and apply the one or more specific actions on the medical data,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to process medical data in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “receiving, pre-processing, transmitting, and processing” as indicated supra. Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a processing circuitry, memory, processor, computing device and non-transitory computer readable medium that implements the identified abstract idea. The server is not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims further recite the additional elements of a client application, interface or library of the client application and chatbot provided by a third party system. The client application, interface or library of the client application and chatbot provided by a third party system. merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processing circuitry, memory, processor, computing device and non-transitory computer readable medium to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a client application, interface or library of the client application and chatbot provided by a third party system. was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such the claims are not patent eligible.
Claims 2-16,19-20 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.
Claim(s) 2 merely describe(s) outputting indications of medical data, which further defines the abstract idea.
Claim(s) 3 merely describe(s) masking personal information in medical data, which further defines the abstract idea.
Claim(s) 4 merely describe(s) determining that the user command contains personal information, which further defines the abstract idea.
Claim(s) 5 merely describe(s) determining an action, which further defines the abstract idea.
Claim(s) 6 merely describe(s) determining an argument, which further defines the abstract idea.
Claim(s) 7-8 merely describe(s) determined actions, which further defines the abstract idea.
Claim 8 also include the additional element of “a chatbot” which is analyzed the same as the “a chatbot” in the independent claims and does not provide a practical application or significantly more for the same reasons.
Claim(s) 9-10, 19-20 merely describe(s) masking personal information, which further defines the abstract idea.
Claim(s) 11 merely describe(s) storing personal information, which further defines the abstract idea.
Claim(s) 12 merely describe(s) processing medical data, which further defines the abstract idea.
Claim 12 also include the additional element of “a chatbot” which is analyzed the same as the “a chatbot” in the independent claims and does not provide a practical application or significantly more for the same reasons.
Claim(s) 13 merely describe(s) storing and processing data, which further defines the abstract idea.
Claim(s) 14-15 merely describe(s) the model, which further defines the abstract idea. T
The claims also include the additional element of “a large language model” which is analyzed the same as the “a chatbot” and does not provide a practical application or significantly more for the same reasons. Claim 14 also include the additional element of “a chatbot” which is analyzed the same as the “a chatbot” in the independent claims and does not provide a practical application or significantly more for the same reasons.
Claim(s) 16 merely describe(s) the medical data, which further defines the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1-4,9-12,14-18 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE et al (US Publication No. 20230274089) in view of Selvaraju et al (US Publication No. 20230359765) in view of Mejias et al (US Publication No. 20200244604).
Regarding Claim 1
TUNSTALL-PEDOE teaches a method for processing medical data by a client application and a chatbot, the method comprising:
receiving, by the client application, a user command comprising natural language, wherein the user command includes personal information [TUNSTALL-PEDOE at Para. 0086-0087 teaches according to a sixteenth aspect of the invention, there is provided a computer-implemented method of improving output using an LLM, including the steps of (i) receiving a natural language question (e.g. any natural language to which a natural language response is appropriate. It could be for example a command or a request for data or even some kind of social interaction or discussion); TUNSTALL-PEDOE at Para. 0185 teaches the computer system may be one wherein the question relates to a request for a compatibility match between persons, wherein the semantic nodes include representations of personal information defining one or more attributes of a person, for a plurality of people], and the user command provides a request to process medical data [TUNSTALL-PEDOE at Para. 0087 teaches receiving a natural language question (e.g. any natural language to which a natural language response is appropriate. It could be for example a command or a request for data or even some kind of social interaction or discussion); TUNSTALL-PEDOE at Para. 0190 teaches the computer system may be one wherein the question relates to health of an individual, wherein the semantic nodes include health data relating to the individual, and health data relating to human beings; TUNSTALL-PEDOE at Para. 0460 teaches in addition to recording health data from wearables and other health sensors, Chea has a chat window where a user can communicate health related events as they happen and have them understood, stored and processed by the application];
TUNSTALL-PEDOE does not teach pre-processing, by the client application, the user command to mask the personal information;
transmitting a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command,
wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receiving a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, and wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument;
and processing the medical data, by the client application, using the identified programmatic feature of the client application, including executing the client application to perform the chatbot-determined action or argument and apply the one or more specific actions on the medical data.
Selvaraju teaches pre-processing, by the client application, the user command to mask the personal information [Selvaraju at Para. 0007 teaches according to an exemplary embodiment of the present inventive concept, a computer system is used to protect sensitive personal information in spoken commands. The system includes one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors capable of performing a method. The method includes identifying sensitive personal information (SPI) included in unmasked portions of at least one classified command and following a user privacy instruction before transmitting the unmasked portions that include the identified SPI to a third-party service];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine chatbot of TUNSTALL-PEDOE with the information masking of Selvaraju with the motivation to improve the scalability, efficiency and accuracy of a text classifier.
TUNSTALL-PEDOE/Selvaraju do not teach transmitting a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command,
wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receiving a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, and wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument;
and processing the medical data, by the client application, using the identified programmatic feature of the client application, including executing the client application to perform the chatbot-determined action or argument and apply the one or more specific actions on the medical data.
Mejias teaches transmitting a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command [Mejias at Para. 0008 teaches a chatbot may interface with an application. For example, a chatbot may be utilized to communicate with an application programming interface (API) of an application that is distinct from the chatbot. In some examples, the chatbot may perform communications such as issue commands to a separate application and/or receive outputs from a separate application. As such, a chatbot may be configured to communicate with an API of a separate application; Mejias at Para. 0030 teaches as such, in the system 100 a chatbot 104 may receive a natural language input 106 from a user],
wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot [Mejias at Para. 0008; Mejias at Para. 0015 teaches however, the chatbot 104 may not be the application and/or a component of the application that has access to the data and/or services being requested by the user. For example, the chatbot 104 may be a stand-alone digital assistant that is not part of the APIs that the user is requesting in their input 106 to be accessed and/or manipulated (chatbot interpreted as a third party)];
receiving a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, and wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument [Mejias at Para. 0016 teaches the chatbot 104 may be a separate generic chatbot that may not be a component of any of the APIs. However, the input 106 from the user to the chatbot 104 may implicate data to be retrieved from, a command to be issued to, a functionality to be performed by, etc. the separate APIs; Mejias at Para. 0030 teaches in some examples, the chatbot 104 may process the input 106 to determine which API 108 the input 106 is directed to. In some examples, the chatbot 104 may infer from the content or context of the input 106 which API 108 the input 106 is directed to. In some examples, the chatbot 104 may prompt and/or receive an indication from the user directly indicating the API 108 that the input 106 is directed to. In some examples, the chatbot 104 may parse the documentation configuration reference of a plurality of APIs to find a match to a regular expression in a particular one of the plurality of APIs that informs which API pf the plurality that the input 106 is directed to];
and processing the medical data, by the client application, using the identified programmatic feature of the client application, including executing the client application to perform the chatbot-determined action or argument and apply the one or more specific actions on the medical data [Mejias at Para. 0037 teaches in some examples, the action 214 performed at the API 214 may generate an output 216. For example, an action 214 such as a data query of a database resource of an API 208 may generate an output 216 including the response to the query (e.g., the queried for data) (interpret to correspond with medical data processing of TUNSTALL-PEDOE)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju with the chatbot of Mejias with the motivation to more useful, efficient and easier to understand chatbot.
Regarding Claim 2
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach comprising:
outputting an indication that the medical data has been processed [TUNSTALL-PEDOE at Para. 0174 teaches the computer system may be configured to output the answer to the question to a display device];
or outputting the processed medical data.
Regarding Claim 3
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 2,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein outputting an indication comprises:
in response to determining, by the client application, that the processed medical data includes personal information [TUNSTALL-PEDOE at Para. 1505 teaches a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language; the structured, machine-readable representation of data including representations of personal health or medical data]:
masking, by the client application, personal information in the processed medical data to produce masked medical data [Selvaraju at Para. 0007 (see Claim 1 for explanation)];
invoking the chatbot to generate an indication based on the masked medical data [TUNSTALL-PEDOE at Para. 0174 (see Claim 2 for explanation)];
and outputting, by the client application, the generated indication [TUNSTALL-PEDOE at Para. 0174 (see Claim 2 for explanation)].
Regarding Claim 4
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein the pre-processing, by the client application, of the user command to mask the personal information is performed in response to determining, by the client application, that the user command includes personal information [Selvaraju at Para. 0007 (see Claim 1 for explanation)].
Regarding Claim 9
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein the pre-processing, by the client application, the user command to mask personal information includes removing the personal information from the user command [Selvaraju at Para. 0007 (see Claim 1 for explanation)].
Regarding Claim 10
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein the pre-processing, by the client application, of the user command to mask personal information includes replacing the personal information with an anonymized text string [Selvaraju at Para. 0037 teaches in an embodiment utilizing the preselected rules/permissions, when the protecting SPI in spoken commands program 134 detects SPI keywords which can compromise privacy, it will automatically anonymize the text. In the above example, the user wants to send a message to his close friend with the help of Gadgets where he provided his address and the passphrase to open his gate. The system will recognize this and replace the address with “Home” and, after user confirmation, send the message to his friend as his friend already knows the address of the user and does not need the home address though the message].
Regarding Claim 11
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 9,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach comprising storing the personal information at the client application [TUNSTALL-PEDOE at Para. 1438 teaches (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language; the structured, machine-readable representation of data including representations of personal information defining one or more of the following attributes of a person: sex, age, information relevant to dating or match-making, information relevant to identifying business connections; information relevant to identifying friends].
Regarding Claim 12
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 11,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach comprising processing medical data, by the client application, using the stored personal information, and the chatbot-determined argument [TUNSTALL-PEDOE at Para. 1506 teaches (b) automatically processing the structured representation to analyse the personal health or medical data (interpreted to correspond with chatbot-determined argument of Mejias)].
Regarding Claim 14
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein the chatbot includes a language model [TUNSTALL-PEDOE at Para. 0061 teaches (ii) Inputting the received output to a large language model (LLM))].
Regarding Claim 15
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 14,
TUNSTALL-PEDOE/Selvaraju/Mejias further wherein the language model includes a generative pre-trained transformer (GPT) [TUNSTALL-PEDOE at Para. 0695 teaches an example of an LLM is GPT3 which has 175 billion parameters and has been trained on approximately 45 terabytes of text (GPT3 interpreted as a generative pre-trained transformer)].
Regarding Claim 16
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein the medical data includes any one or more of electronic medical records, electronic health records, treatment plans, patient data, and medical imaging data [TUNSTALL-PEDOE at Para. 1506 (see Claim 12 for explanation; medical data interpreted as patient data)].
Regarding Claim 17
TUNSTALL-PEDOE teaches a system for processing medical data by a client application and a chatbot, the system comprising:
processing circuitry [TUNSTALL-PEDOE at Para. 0194 teaches 203. A computer-implemented method, the method using a computer system including a processor and a memory, the processor configured to use a processing language in which semantic nodes are represented in the processing language, the semantic nodes including semantic links between semantic nodes wherein the semantic links are themselves semantic nodes, in which each semantic node denotes one specific meaning, in which a combination of semantic nodes defines a semantic node, in which expressions in the processing language may be nested, in which the question is represented in the processing language, in which reasoning steps are represented in the processing language to represent semantics of the reasoning steps, in which computation units are represented in the processing language, wherein the memory is configured to store the representations in the processing language, the method including the steps of]; and
memory, including instructions stored thereon, which, when executed by the processing circuitry, cause the processing circuitry to [TUNSTALL-PEDOE at Para. 0194]:
receive, by the client application, a user command comprising natural language [TUNSTALL-PEDOE at Para. 0086-0087, 0185 (see Claim 1 for full explanation)], wherein the user command includes persona! information, and the user command provides a request to process medical data [TUNSTALL-PEDOE at Para. 0087, 0190, 0460 (see Claim 1 for full explanation)];
TUNSTALL-PEDOE does not teach pre-process, by the client application, the user command to mask the personal information;
transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command. wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data.
Selvaraju teaches pre-process, by the client application, the user command to mask the personal information [Selvaraju at Para. 0007 (see Claim 1 for full explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine chatbot of TUNSTALL-PEDOE with the information masking of Selvaraju with the motivation to improve the scalability, efficiency and accuracy of a text classifier.
TUNSTALL-PEDOE/Selvaraju do not teach transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command, wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data.
Mejias teaches teach transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command [Mejias at Para. 0008, 0030 (see Claim 1 for full explanation)], wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot [Mejias at Para. 0008, 0015 (see Claim 1 for full explanation)];
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument [Mejias at Para. 0016, 0030 (see Claim 1 for full explanation)]; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data [Mejias at Para. 0037 (see Claim 1 for full explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju with the chatbot of Mejias with the motivation to more useful, efficient and easier to understand chatbot.
Regarding Claim 18
TUNSTALL-PEDOE teaches a non-transitory computer-readable medium with instructions stored thereon that, when executed by a processor of a computing device, cause the processor to:
receive, by the client application, a user command comprising natural language [TUNSTALL-PEDOE at Para. 0086-0087, 0185 (see Claim 1 for full explanation)], wherein the user command includes persona! information, and the user command provides a request to process medical data [TUNSTALL-PEDOE at Para. 0087, 0190, 0460 (see Claim 1 for full explanation)];
TUNSTALL-PEDOE does not teach pre-process, by the client application, the user command to mask the personal information;
transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command. wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data.
Selvaraju teaches pre-process, by the client application, the user command to mask the personal information [Selvaraju at Para. 0007 (see Claim 1 for full explanation)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine chatbot of TUNSTALL-PEDOE with the information masking of Selvaraju with the motivation to improve the scalability, efficiency and accuracy of a text classifier.
TUNSTALL-PEDOE/Selvaraju do not teach transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command, wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot;
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data.
Mejias teaches teach transmit a prompt to the chatbot, the prompt invoking the chatbot to determine an argument to use in the client application based on information provided from the pre-processed user command [Mejias at Para. 0008, 0030 (see Claim 1 for full explanation)], wherein the chatbot is provided by a third party system and the medical data is not accessible to the chatbot [Mejias at Para. 0008, 0015 (see Claim 1 for full explanation)];
receive a chatbot-determined argument from the chatbot, wherein the chatbot-determined argument identifies a programmatic feature available to be programmatically invoked in an interface or library of the client application, and the programmatic feature executes one or more functions, methods, services, or components provided by or controlled by the client application, wherein the one or more functions, methods, services or components are identified by the chatbot to apply one or more specific actions to input data based on the chatbot-determined argument [Mejias at Para. 0016, 0030 (see Claim 1 for full explanation)]; and
process the medical data, by the client application, using the identified programmatic feature of the client application. including executing the client application to perform the chatbot-determined argument and apply the one or more specific actions on the medical data [Mejias at Para. 0037 (see Claim 1 for full explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju with the chatbot of Mejias with the motivation to more useful, efficient and easier to understand chatbot.
Claims 5-6 are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju, Mejias as applied to claim 1 above, and further in view of Bharara et al (US Publication No. 20200349228).
Regarding Claim 5
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias do not teach wherein the chatbot is configured to:
determine an action from the pre-processed user command by comparing the pre-processed user command to a set of reference actions.
Bahara teaches the chatbot is configured to:
determine an action from the pre-processed user command by comparing the pre-processed user command to a set of reference actions [Bahara at Para. 0099 teaches the sequence (or order, or hierarchy, etc.) of the intents in the command tree may be analyzed at 428. Analyzing the sequence of intents at 428 may include comparing the intent structure (e.g. sequence) in the command tree against the sequence or structure of previously executed intents. For example, a record of mapped intents and their sequence or structure may be maintained, and the current intent sequence may be compared against this intent sequence repository to determine if the current intent sequence, or similar sequences, has previously been executed].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju, Mejias with the comparison of Bharara with the motivation to improve future command translations.
Regarding Claim 6
TUNSTALL-PEDOE/Selvaraju/Mejias/Bahara teach the method of claim 5,
TUNSTALL-PEDOE/Selvaraju/Mejias/Bahara further teach wherein the chatbot is configured to: determine an argument from the pre-processed user command by comparing the pre-processed user command to a set of reference arguments [Bahara at Para. 0099 (see Claim 5 for explanation)].
Claims 7 are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju, Mejias as applied to claim 1 above, and further in view of DONG et al (Foreign Publication CN-116884559-A).
Regarding Claim 7
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju/Mejias do not teach wherein the chatbot-determined action includes any one or more of:
searching for patient records, querying patient records, creating a collection of patient records, modifying patient records, retrieving medical images, or searching a data source for an item.
DONG teaches wherein the chatbot-determined action includes any one or more of:
searching for patient records, querying patient records, creating a collection of patient records, modifying patient records, retrieving medical images, or searching a data source for an item [DONG at Page 13 Para 12 teaches the first medical data refers to information data related to medical image data obtained after the medical examination procedure has been completed. After the medical examination procedure has been completed, that is, after the medical image data (e.g., the image is seen) is acquired, the doctor or related person has completed the evaluation of the image's view, and an image report is obtained].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju, Mejias with the medical image retrieval of DONG with the motivation to improve the processing effect and performance of the generated model.
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju, Mejias, DONG as applied to claim 7 above, and further in view of JEONG et al (Foreign Publication KR20220050678A ).
Regarding Claim 8
TUNSTALL-PEDOE/Selvaraju/Mejias/DONG teach the method of claim 7,
TUNSTALL-PEDOE/Selvaraju/Mejias/DONG do not teach comprising, in response to the chatbot-determined action including searching a data source:
invoking the chatbot to determine an embedding that corresponds to the item to be searched;
comparing, by the client application, the determined embedding with a set of reference embeddings;
in response to the comparing, detem1ining, by the client application, a candidate embedding;
and retrieving, by the client application, information corresponding to the candidate embedding from the data source.
JEONG teaches comprising, in response to the chatbot-determined action including searching a data source:
invoking the chatbot to determine an embedding that corresponds to the item to be searched [JEONG at Page 2 Para 1 teaches according to an embodiment of the present specification, there is provided a method for a chatbot driving device to provide a chatbot service to a user using a multiple search method, the method comprising: setting question data and response data in a database; embedding the question data using 1) a dense vector, and 2) a sparse vector; receiving a question input from the user; selecting a candidate group of the question from the database based on a density vector of the question through a density-embedding based retrieval (DR) model; and selecting a member with the highest similarity among members of the candidate group based on the sparse vector of the question through a sparse-embedding based retrieval (SR) model. may include];
comparing, by the client application, the determined embedding with a set of reference embeddings [JEONG at Page 3 Para 12 teaches here, both DR and SR may have an embedding process and a cosine similarity comparison process. For example, the medical chatbot device 300 calculates the cosine similarity between the dense vector of the embedded question data and the dense vectors of the question input from the user through the DR model, , N=10) correct answer candidate sentences can be delivered as an input of the SR model];
in response to the comparing, detem1ining, by the client application, a candidate embedding [JEONG at Page 3 Para 9 teaches the medical chatbot device 300 selects a candidate group of the question based on the dense vector of the question through Dense-embedding based Retrieval (DR) ( S540 ). For example, the medical chatbot device 300 may select a candidate group of the question from question data in the database];
and retrieving, by the client application, information corresponding to the candidate embedding from the data source [JEONG at Page 3 Para 14 teaches the medical chatbot device 300 selects an answer to a question corresponding to a member with the highest similarity through the database (S560)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju, Mejias, DONG with the embedding of JEONG with the motivation to improve the analysis result of the neural network model.
Claims 13, 19, 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju, Mejias as applied to claim 1 above, and further in view of Zahlmann et al (US Publication No. 20050267782).
Regarding Claim 13
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 10,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach comprising:
storing the personal information at the client application [Selvaraju at Para. 0038 teaches the user’s data stored in the protecting SPI in spoken commands repository 132 may be encrypted], … [ … ]
TUNSTALL-PEDOE/Selvaraju do not teach [ … ] … and wherein processing the medical data, by the client application, includes replacing the anonymized text string with the stored personal information.
Zahlmann teaches [ … ] … and wherein processing the medical data, by the client application, includes replacing the anonymized text string with the stored personal information [Zahlmann at Para. 0021 teaches in another embodiment, application 20 allocates a second patient identifier to individual removed site identification records that are stored in database DB2 and to patient and site non-specific medical information that are stored in database DB1. In this embodiment, application 20 at a service provider location uses the allocated second patient identifiers to re-associate healthcare provider organizations and their site locations (using information in database DB1) with clinical data in DB2. Application 37 at a healthcare provider uses the new patient identifiers allocated by application 37 to re-associate this clinical data (and re-associated site location information) with individual patients using patient identification information in database DB3].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju, Mejias with the replacement of Zahlmann with the motivation to improve data processing for patient medical data in clinical trials.
Regarding Claim 19
TUNSTALL-PEDOE/Selvaraju/Mejias teach the method of claim 17,
TUNSTALL-PEDOE/Selvaraju/Mejias further teach wherein to pre-process, by the client application, the user command to mask personal information includes to store the personal information at the client application [Selvaraju at Para. 0038 teaches the user’s data stored in the protecting SPI in spoken commands repository 132 may be encrypted] and to replace the personal information with an anonymized text string [Selvaraju at Para. 0037 teaches in an embodiment utilizing the preselected rules/permissions, when the protecting SPI in spoken commands program 134 detects SPI keywords which can compromise privacy, it will automatically anonymize the text. In the above example, the user wants to send a message to his close friend with the help of Gadgets where he provided his address and the passphrase to open his gate. The system will recognize this and replace the address with “Home” and, after user confirmation, send the message to his friend as his friend already knows the address of the user and does not need the home address though the message. In this way, the user’s SPI will not be sent to the speech recognition software 123, hence providing a safety guard to the user];
TUNSTALL-PEDOE/Selvaraju does not teach and wherein to process the medical data, by the client application, includes to replace the anonymized text string with the stored personal information, and to use the stored personal information in the chatbot-determined action or argument.
Zahlmann teaches and wherein to process the medical data, by the client application, includes to replace the anonymized text string with the stored personal information, and to use the stored personal information in the chatbot-determined action or argument [Zahlmann at Para. 0021 (see Claim 31 for explanation; interpret to combine with chatbot of Selvaraju)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of TUNSTALL-PEDOE, Selvaraju, Mejias with the replacement of Zahlmann with the motivation to improve data processing for patient medical data in clinical trials.
Regarding Claim 20
Claim(s) 20 is/are analogous to Claim(s) 19, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 19.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claim 1-20, the Examiner has reconsidered the rejection in light of the 2019 Revised Patent Subject Matter Eligibility Guidance dated January 7, 2019 and withdraws the rejection. The claimed invention is subject matter eligible because…
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
First, the abstract idea rejection is rendered moot in light of the present amendments to the independent claims that recite the elements of identifying "a programmatic feature available to be programmatically invoked in an interface or library of the client application" and then "using the identified programmatic feature of the client application, including executing the client application.'' The claims unambiguously recite operations with a practical application: to programmatically control software functions that perform certain actions in a software application. The claims recite how natural language user commands are converted into chatbot prompts, and then used to invoke a precise programmatic feature as an argument to an interface or library. This level of automation separates the claimed approach from mere human-driven programming, generic chatbot interaction, or human software interactions.
Regarding (a), the Examiner respectfully disagrees. Computers performing their normal functions of executing client applications does not recite an improvement to the computer or technological environment.
On Page 22 of the Final Office Action, the Examiner alleged that ''the basis of rejection the identified abstract represents a series of rules or instructions for a person or persons, with or without the aid of a computer, to follow to process medical data.'' This interpretation of an "abstract idea" cannot be read on the pending claims, which expressly recite programmatically invoked functions and programmatic features in software. The claimed operations cannot be performed "without" a computer. The claimed operations recite computer programming operations that occur based on chatbot (computer) determined outputs, without human intervention or actions.
Regarding (b), the Examiner respectfully disagrees. receiving a user command is human interaction. Furthermore, if the user isn’t a person, Multiple CAFC decisions that the Office has characterized as Certain Method of Organizing Human Activity did not actively recite a person or persons performing the steps of the claims (see, e.g., EPG, TLI communications, Ultramercial). Because whether a human is required to perform the step of the claim is not a requirement for claims to encompass certain method of organizing human activity, this argument is not persuasive.
On Page 23 of the Final Office Action, the Examiner also alleged that "claims do not identify nor provide any further information on the supposed 'functions, methods, services or components' argued by the Applicant." The claims now recite more details of programmatically invoking features of a software application, the use of a specific interface or library of this software application, and how the "functions. methods, services or components" will "apply one or more specific actions to input data based on the chatbot-determined argument." A person of ordinary skill in the art, familiar with software programming capabilities, would recognize the resulting automation and technical improvement to software that is accomplished with this approach.
Regarding (c), the Examiner respectfully disagrees. No specific library or interface in mentioned. Furthermore, there is no technical problem with the chatbot or the software application mentioned. Invoking a computer or software application to do what it normally does is not an improvement, as previously stated. Therefore, no practical application is found.
In response, Applicant refers the Examiner to revised MPEP sections and updated Examination guidance that was recently issued by the Patent Office in light of the EX Parte Desjardins precedential decision.1 As explained in the Office's recent memorandum about these changes, a "practical application" can be evidenced by an improvement to "technology or a technical field. "2 The relevant section of MPEP § 2106 now explains that when evaluating a claim for a practical application, … [ … ] … Because the claim is directed to an improved method of processing medical data with a software application, the resulting "computer system" with all of its accompanying software functionality must be considered. The recited computer operations are the focus of the claimed invention, rather than a mere "tool" for achieving a result The resulting technical improvement of improved privacy and security is needed for the software to correctly function in a medical setting.
Regarding (a), the Examiner respectfully disagrees. The Examiner respectfully submits that there is no improvement to the claim machine learning as there is in Desjardins. As found by the Panel, the claimed “training strategy allows the model to preserve performance on earlier tasks even as it learns new ones, directly addressing the technical problem of 'catastrophic forgetting' in continual learning systems" represents “technical improvements over conventional systems by addressing challenges in continual learning and model efficiency by reducing storage requirements and preserving task performance across sequential training.” This analysis represents implementation of the practical application-“improvement” analysis of MPEP 2106.04(d)(I) to the facts before the Panel.
Applicant’s claims do not provide such an improvement. Initially, there is no training within the claims so this argument falls on its face from the outset. Even assuming there was, there is no indication in the cited portion of the Specification that the claimed invention provides an improvement as to how model is trained. Improving the accuracy of a machine learning model by supplying it with specific data is not an improvement to how the model is trained within the meaning of Desjardins (see quotations from Recentive, infra). This is how all machine learning models are optimized (i.e., select training data, train the model, compare the output to validation data, receive feedback, adjust the parameters of the training data according to the comparison/feedback, and repeat until an accuracy threshold is met). Put another way, the particular way the machine learning model of applicant’s invention uses the data to train itself is not improved, which is the holding of Desjardins. Applicant is merely improving the accuracy of the model by optimizing the data selected/used by the model. Improving the accuracy of a model is not an improvement by any measure in MPEP 2106.
Examiner’s position is also supported by the decision in Recentive Analytics, Inc. v. Fox Corp. Recentive held that non-specifically claimed training of an AI algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as afforded by the present RCE.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
KONIG et al (Foreign Publication EP-3808065-B1) discloses a method for automating interactions with enterprises.
LANGE et al (Foreign Publication WO-2023154392-A1) discloses a system for navigating a conversation graph using a language model trained to generate Application Programming Interface (API) calls in response to natural language input from a user computing device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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