Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim(s) 19-20 are new
Claim(s) 1, 3-4, 7-10, 12-18 are amended
Claim(s) 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. The claim recites a method, system and non-transitory computer-readable medium, which are within a statutory category. The limitations of:
Claims 1, 17-18 (Claim 1 being representative)
receiving a user command comprising natural language, where in 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;
determine an action or an argument from the pre-processed user command, wherein the determined action or argument identifies a feature available to be used and the feature includes one or more functions, methods, services, or components provided by or controlled;
and processing the medical data using the identified feature to perform determined action or argument 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. That is, other than reciting a processing circuitry, memory, processor, computing device and non-transitory computer readable medium, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the processing circuitry, memory, processor, computing device and non-transitory computer readable medium, this claim encompasses processing medical data in the manner described in the identified abstract idea, supra. 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 claims recite an abstract idea.
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, each having a client application that implements the identified abstract idea. The processing circuitry, memory, processor, computing device and non-transitory computer readable medium are 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 claim is directed to an abstract idea.
The claims further recite the additional elements of a client application and chatbot. The client application and chatbot 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.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional 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 and chatbot 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. For completeness, the Examiner notes that the prior art of record indicates that chatbots are well-understood, routine, and conventional in the art (see, e.g., US2022/0229993 to Vu et al. at Para. 0028; US2020/0228470 to Koo et al. at Para. 0003; US2019/0142062 to Dayama et al. at Para. 0070). Accordingly, even in combination, this additional element does not 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. 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 1-4,9-12,16-18 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).
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, where in 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];
invoking the chatbot to determine an action or an argument from the pre-processed user command, wherein the chatbot-determined action or argument identifies a feature available to be used in the client application, and the feature includes one or more functions, methods, services, or components provided by or controlled by the client application [TUNSTALL-PEDOE at Para. 1020 teaches Large language models have the helpful property, particularly if so trained, of being able to be instructed in the context (or “prompt”) and the text they then generate extending the context thus often obeys that instruction and can answer general questions or do a variety of different tasks typically previously seen in the text they have been trained on. Useful behaviours can thus be delivered by these models simply by creating a suitable prompt. In some cases models that have been trained on huge amounts of general text can be further improved by fine tuning the model on text that contains many examples of the desired task. Small numbers of examples can also be included in the prompt; TUNSTALL-PEDOE at Para. 1751 teaches the computer system comprises a component which generates candidate actions; a component that decides whether to execute the candidate actions with reference to the tenets and a component which executes actions];
and processing the medical data, by the client application, using the identified feature of the client application to perform the chatbot-detem1ined action or argument on the medical data [TUNSTALL-PEDOE at Para. 1506 teaches (b) automatically processing the structured representation to analyse the personal health or medical data] .
TUNSTALL-PEDOE does not teach pre-processing, by the client application, the user command to mask the personal information;
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.
Regarding Claim 2
TUNSTALL-PEDOE/Selvaraju teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 2,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 9,
TUNSTALL-PEDOE/Selvaraju 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 teach the method of claim 11,
TUNSTALL-PEDOE/Selvaraju further teach comprising processing medical data, by the client application, using the stored personal information, and the chatbot-determined action or argument [TUNSTALL-PEDOE at Para. 1506 teaches (b) automatically processing the structured representation to analyse the personal health or medical data].
Regarding Claim 16
TUNSTALL-PEDOE/Selvaraju teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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, wherein the user command includes personal information [TUNSTALL-PEDOE at Para. 0086-0087, 0185 (see Claim 1 for full explanation)], and the user command provides a request to process medical data [TUNSTALL-PEDOE at Para. 0087, 0190, 0460 (see Claim 1 for full explanation)];
invoking the chatbot to determine an action or an argument from the pre-processed user command, wherein the chatbot-determined action or argument identifies a feature available to be used in the client application, and the feature includes one or more functions, methods, services, or components provided by or controlled by the client application [TUNSTALL-PEDOE at Para. 1020, 1751 (see Claim 1 for full explanation)];
and process the medical data, by the client application, using the identified feature of the client application to perform the chatbot-determined action or argument on the medical data [TUNSTALL-PEDOE at Para. 1506 (see Claim 1 for full explanation)].
TUNSTALL-PEDOE does not teach pre-processing, by the client application, the user command to mask the personal information;
Selvaraju teaches pre-processing, 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.
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 a client application, a user command comprising natural language, wherein the user command includes personal information [TUNSTALL-PEDOE at Para. 0086-0087, 0185 (see Claim 1 for full explanation)], and the user command provides a request to process medical data [TUNSTALL-PEDOE at Para. 0087, 0190, 0460 (see Claim 1 for full explanation)];
invoking a chatbot to determine an action or an argument from the pre-processed user command, wherein the chatbot-determined action or argument identifies a feature available to be used in the client application, and the feature includes one or more functions, methods, services, or components provided by or controlled by the client application [TUNSTALL-PEDOE at Para. 1020 (see Claim 1 for full explanation)];
and process the medical data, by the client application, using the identified feature of the client application to perform the chatbot-determined action or argument on the medical data [TUNSTALL-PEDOE at Para. 1506 (see Claim 1 for full explanation)].
TUNSTALL-PEDOE does not teach pre-processing, by the client application, the user command to mask the personal information;
Selvaraju teaches pre-processing, 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.
Claims 5-6 are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju as applied to claim 1 above, and further in view of Bharara et al (US Publication No. 20200349228).
Regarding Claim 5
TUNSTALL-PEDOE/Selvaraju teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 with the comparison of Bharara with the motivation to improve future command translations.
Regarding Claim 6
TUNSTALL-PEDOE/Selvaraju/Bahara teach the method of claim 5,
TUNSTALL-PEDOE/Selvaraju/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 7,14-15 are rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-EDOE, Selvaraju 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 teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju 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 with the medical image retrieval of DONG with the motivation to improve the processing effect and performance of the generated model.
Regarding Claim 14
TUNSTALL-PEDOE/Selvaraju teach the method of claim 1,
TUNSTALL-PEDOE/Selvaraju do not teach wherein the chatbot includes a language model.
DONG teaches wherein the chatbot includes a language model [DONG at Page 2 Para 7 teaches in some embodiments, the pre-training Model may be a Language Model (LM) obtained through a pre-training stage].
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 model of DONG with the motivation to improve the processing effect and performance of the generated model.
Regarding Claim 15
TUNSTALL-PEDOE/Selvaraju/DONG teach the method of claim 14,
TUNSTALL-PEDOE/Selvaraju/DONG further teach wherein the language model includes a generative pre-trained transformer (GPT) [DONG at Page 2 Para 8 teaches in some embodiments, the pre-training model may be a large language model obtained through a pre-training phase. Exemplary large language models include BERT (Bidirectional Encoder Representation from Transformers), GPT (generated Pre-trained Transformer), XLNet, chatGLM-6B models, and the like].
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over TUNSTALL-PEDOE, Selvaraju, DONG as applied to claim 7 above, and further in view of JEONG et al (Foreign Publication KR20220050678A ).
Regarding Claim 8
TUNSTALL-PEDOE/Selvaraju/DONG teach the method of claim 7,
TUNSTALL-PEDOE/Selvaraju/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/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 as applied to claim 1 above, and further in view of Zahlmann et al (US Publication No. 20050267782).
Regarding Claim 13
TUNSTALL-PEDOE/Selvaraju teach the method of claim 10,
TUNSTALL-PEDOE/Selvaraju 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 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 teach the method of claim 17,
TUNSTALL-PEDOE/Selvaraju 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 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 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:
Applicant respectfully traverses this interpretation, based on the recitations in amended independent claims 1, 17, and 18 that perform a real-world (and not mental) action in a computer, including that the "the chatbot-determined action or argument identifies a feature available to be used in the client application, and the feature includes one or more functions, methods, services, or components provided by or controlled by the client application" and "processing the medical data, by the client application, using the identified feature of the client application." No reasonable reading of these claims would conclude a human is able to operate a chatbot in their mind, or process medical data with functions, methods, services, or components of a client application. Further, no reasonable reading of the other claimed elements such as "receiving" or "pre- processing" by a client application can be performed with human behavior or thought. The recitations of the claims cannot be simplified to mere human activity.
The rejection neglects to give any patentable weight to the technical context of controlling a software client application in a computing system. Instead, the Office Action alleges that the claim's "circuitry, memory, processor, computing device and non-transitory computer readable medium are not described by the applicant and is recited at a high-level of generality." This is easily rebutted when recognizing that the recited "feature" is not some generic aspect of a computer, but rather involves one or more specially identified "functions, methods, services, or components." Using such features goes beyond mere instructions to use or apply a computer, but provides a custom control of how the software and the computer itself works and processes certain medical data.
The claimed methods and systems recite an improved processing of medical data that still enables chatbot-determined actions or arguments with software features, while preserving technical requirements for privacy and security. An improved method of invoking software application functions, methods, services, or components based on chatbot outputs, while also maintaining security and data confidentiality, would undoubtedly qualify as an improvement to the functioning of a technology and technical field.
Regarding (a), the Examiner respectfully disagrees. Examiner never mentioned the claims as being performed in the human mind and thus Applicant’s arguments directed to this are immaterial. Further, and as indicated in 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 (see preamble). The Applicant appears to be confusing the mental process abstract idea characterization (which was not relied upon) with the certain methods of organizing human activity characterization (which was relied upon).
Regarding (b), inasmuch as the Examiner understands what the Applicant is arguing, the Examiner respectfully disagrees. The claims do not identify nor provide any further information on the supposed “functions, methods, services or components” argued by the Applicant. The “client application” is interpreted to be part of the general-purpose computer (see Spec. Para. 016, 0043); every general-purpose computer includes applications (i.e., programs). The actual functions of the client application are the abstract idea. To the extent that the Applicant may be arguing that a special-purpose computer is present (i.e., a general purpose computer with specialized programming), MPEP 2106(I) states that “[t]he programmed computer or ‘special purpose computer’ test of In re Alappat, 33 F.3d 1526, 31 USPQ2d 1545 (Fed. Cir. 1994) (i.e., the rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim for the ‘special purpose’ of executing the algorithm or software) was superseded by the Supreme Court’s Bilski and Alice Corp. decisions.
Regarding (c), the Examiner respectfully disagrees. The functions of the claim do not physically improve the computer. “[P]reserving technical requirements for privacy and security” is an improvement to the abstract idea and not a physical improvement to the computer. And, no other technology is improved because no other technology is recited in the claims.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Applicant argues:
Amended limitations added to independent claims overcome prior art of record.
A person of ordinary skill in the art would not be motivated to used to Tunstall-Pedoe’s chatbot.
Regarding (a), the Examiner respectfully disagrees. Under the broadest reasonable interpretation, the newly added limitations do not provide anything to overcome the prior art of TUNSTALL-PEDOE and Selvaraju. Specifically, TUNSTALL-PEDOE at Para. 0087 recite inputs of natural language as a question, which literally discloses the example of a command, which covers the limitation of a user command. In combination with the other limitations of TUNSTALL-PEDOE at 0190, 460, the prior is interpreted to process medical data. Furthermore, the claims do not specify the specific features available to be used in the client. Therefore, the features are given their broadest reasonable interpretation, is interpreted to be covered by TUNSTALL-PEDOE at 1020, 1751
Regarding (b), the Examiner respectfully disagrees. A motivation is not needed where the feature in question is taught by the anticipatory (base) reference.
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:
NOHARA et al (US Publication No. 20200218826) discloses a system, method and program for confidentially searching medical data.
CHEONG et al (Foreign Publication KR-20220048554-A) discloses a method for driving a medical chatbot system that provides medical information in a question-and-answer manner.
THIS ACTION IS MADE FINAL. 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 extension fee 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.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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