DETAILED ACTION
This communication is in response to the Application filed on 11/08/2023 (foreign priority). Claims 1-20 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Chinese Application No. 202311485106.0 and Chinese Application No. 202311570265.0, filed on November 8, 2023, and November 22, 2023, respectively.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/08/2024, 06/02/2025, 12/03/2025, and 06/12/2026 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim(s) 7, and 16 objected to because of the following informalities:
... wherein the respective message presentation modes of the plurality of interaction channels are determined based on respective conversational user interface (CUI) capabilities of the plurality of interaction channels.
Appropriate correction is required.
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim(s) 1, 10, and 19, the limitation(s) “receiving, in an interaction between a user and a digital assistant, a conversation message of the user for the digital assistant,” “determining a processing result for the conversation message by a model, the processing result indicating target content matching a user requirement corresponding to the conversation message,” and “presenting, based on a first message presentation mode corresponding to an interaction channel in which the conversation message is received, a reply message of the digital assistant for the conversation message in the interaction channel, the reply message presenting at least a portion of the target content,” as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a digital assistant,” “at least one processing unit,” “at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit,” and “a non-transitory computer-readable storage medium having a computer program stored thereon,” and specifically for claim(s) 1, 10 and 19, respectively, nothing in the claims’ elements preclude the steps from being practically performed in the mind. Specifically, but not including the generic computer components, the mental processes of a human hearing or reading a question from another person, thinking about what the question means and deciding what information would answer it, and answering the question by speaking or writing a manner appropriate to the setting in which the question was asked. If a limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim(s) recites(s) an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claim(s) 1, 10 and 19 recite(s) the four additional elements — a digital assistant, at least one processing unit, at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, and a non-transitory computer-readable storage medium having a computer program stored thereon.
The digital assistant is recited with a high level of generality (see [0029], where the digital assistant 122 may be configured to have an intelligent session. In the example shown in FIG. 1, the digital assistant 122 may be integrated into the target application 120 as a part of the target application 120 to assist in performing task processing within the target application 120. In other examples, the digital assistant 122 may be configured as an application that runs independently, such as a web application or other types of application. In such an example, the digital assistant 122 and the target application 120 may be considered as the same application. The digital assistant 122 is provided to assist the user in various types of task processing requirements in different applications and scenarios. During interaction with the digital assistant 122, the user inputs an interaction message and in response to the user input, the digital assistant 122 provides a reply message. Generally, the digital assistant 122 can support users to input questions in natural language and perform tasks and provide replies based on understanding of natural language input and logical reasoning capabilities).
Furthermore, the at least one processing unit is recited with a high level of generality (see [0088], where the processing unit 610 may be a physical or virtual processor and can perform various processing according to a program stored in the memory 620).
Moreover, the at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit is recited with a high level of generality (see [0089], where the electronic device 600 typically includes a plurality of computer storage medium. Such media may be any available media that are accessible by the electronic device 600, including, but not limited to, volatile and non-volatile media, removable and non-removable media. The memory 620 may be a volatile memory (e.g., a register, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 630 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium that can be used to store information and/or data and that can be accessed within the electronic device 600).
Additionally, the non-transitory computer-readable storage medium having a computer program stored thereon is recited with a high level of generality (see [0090], where the electronic device 600 may further include additional detachable/undetachable, volatile/nonvolatile storage medium. Although not shown in FIG. 6, a magnetic disk drive for reading from or writing to a detachable, nonvolatile magnetic disk, such as a "floppy disk" and an optical disk drive for reading from or writing to a detachable, nonvolatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 620 may include a computer program product 625 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, and concerning claim(s) 1, 10 and 19 alone, the additional elements of a digital assistant, at least one processing unit, at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, and a non-transitory computer-readable storage medium having a computer program stored thereon to perform the receiving, determining, and presenting steps amount to no more than mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 2, 11, and 20, the claim(s) recite(s) “wherein the processing result is represented in a second computer language,” “determining a first computer language supported by the interaction channel based on the first message presentation mode,” “converting the processing result from the second computer language to description information in the first computer language,” and “presenting the reply message based on the description information,” which reads on a human forming the answer in an internal mental representation (e.g., a silent thought in one’s own mental language); recognizing the format the listener expects (e.g., spoken words, written sentences, bullet points, a drawn chart); and translating the internal thought into that expected format; and delivering the answer in that format. No additional limitations are present.
With respect to claim(s) 3, and 12, the claim(s) recite(s) “wherein the model is trained based on the second computer language,” which reads on a human learning, though practice, to think and express answers in a particular format or language. No additional limitations are present.
With respect to claim(s) 4, and 13, the claim(s) recite(s) “wherein the second computer language comprises a markup language,” which reads on a human using a structured written format with labels or markers to organize an answer, such as writing headings, bullets points, numbered lists, Unicode, HTML, XML, etc. No additional limitations are present.
With respect to claim(s) 5, and 14, the claim(s) recite(s) “wherein the digital assistant is capable of being triggered for interaction in a plurality of interaction channels,” and “wherein the plurality of interaction channels has respective message presentation modes and support respective computer languages,” which reads on a human being able to converse in more than one setting (e.g., in person, or by written note); and recognizing the different formats for each setting expects (e.g. spoken words, written words) knowing which formats are appropriate for each setting. No additional limitations are present.
With respect to claim(s 6, and 15, the claim(s) recite(s) “determining, from the plurality of interaction channels, the interaction channel in which the conversation message is received,” and “presenting, in the interaction channel, the reply message of the digital assistant for the conversation message based on the first message presentation mode corresponding to the determined interaction channel,” which reads on a human noticing which setting the question came in through (e.g., words spoken to them, words written in a piece of paper or text); and responding in that same setting using the appropriate format to it as required. No additional limitations are present.
With respect to claim(s) 7, and 16 the claim(s) recite(s) “wherein the respective message presentation modes of the plurality of interaction channels are determined based on respective conversational user interface (CUI) capabilities of the plurality of interaction channels,” which reads on a human recognizing what each conversational setting allows (e.g., knowing that an in-person meeting permits voice, gesture, and facial expression, and a written letter permits text and drawn figures); and selecting the presentation and setting mode accordingly. No additional limitations are present.
With respect to claim(s) 8, and 17 the claim(s) recite(s) “wherein the processing result indicates presenting the target content in a target data statistical form,” “determining, in the first message presentation mode, a presentation style predefined for the target data statistical form,” and “presenting the target content in the reply message according to the presentation style,” which reads on a human deciding that the answer is best expressed as a statistical chart or table; selecting a conventional format for that chart or table (e.g., a bar chart or table with headers); and drawing the chart or table by hand according to the conventional format. No additional limitations are present.
With respect to claim(s) 9, and 18, the claim(s) recite(s) “wherein the target data statistical form comprises at least one of the following: a chart type, or a form type,” which reads on a human choosing between drawing a chart (e.g., bar chart, line chart, etc.) or filling out a form (a table, a form with labeled fields, etc.) to present the data. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-7, 10-11, 14-16, and 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dhotre et al. (US 12524623 B2).
Regarding claim(s) 1, 10, and 19, Dhotre teaches a method for message processing, comprising:
receiving, in an interaction between a user and a digital assistant, a conversation message of the user for the digital assistant (see Column 6, lines 9-12, where each NLU provider of the NLU providers 10a-x can receive query information and output intent data. As an example, the NLU provider 110a can receive a natural language query. Also see Column 8, lines 37-44, where the method 600 is instantiated by receiving a query (step 602). The digital assistant platform 108 can use the adapters 204 to receive the query. The query can be a natural language query, and it can be a voice-based query or a text-based query. The query can be sent by the user device 102);
determining a processing result for the conversation message by a model, the processing result indicating target content matching a user requirement corresponding to the conversation message (see Column 6, lines 12-23, where the NLU provider 110a can receive a natural language query, and then the NLU provider 110a can, using a machine-learned model, process the natural language query. In processing the natural language query, the NLU provider 110a can discern one or more intents of the natural language query. Furthermore, in addition to discerning one or more intents of the natural language query, the NLU provider 110a can discern attributes of those intent. The NLU provider 110a can then structure and output intent data, which includes the one or more intents and the attributes of those intents. An example of intent data is example intent data 804 of FIG. 8, which is further described below); and
presenting, based on a first message presentation mode corresponding to an interaction channel in which the conversation message is received, a reply message of the digital assistant for the conversation message in the interaction channel, the reply message presenting at least a portion of the target content (see Column 9, lines 45-60, where the digital assistant platform 108 can transmit the intent data to a handler of the handlers 112a-x (step 612). To do so, the orchestrator 202 can pass the intent data to the intent routers 208. The intent routers 208 can then read the intent of the intent data, determine which handler of handlers 112a-x is associated with that intent in the handler database 212, and transmit the intent data to that handler. As discussed in connection to FIG. 5, the digital assistant platform 108 can transmit more data than only the intent data to the handlers. For example, the digital assistant platform can transmit information related to previous intents, information related to previous queries, information related to previous content delivered to the user device 102, information associated with the user device 102, information related to the device type of the user device 102, and other information that can be used by the handlers 112a-x. Also see Column 10, lines 1-11, where the digital assistant platform 108 can also adapt the content to fit the user device 102 (step 616). The digital assistant platform 108 can use the adapters 204 to adapt the content. For example, the adapters 204 can, if the user device 102 is a device without a display screen, adapt the content so that it provides voice-based responses and contains less information. As a further example, the adapters 204 can, if the user device is a device using a native mobile application, adapt the response to include both voice and text response, as well as a button for the user of the user device 102 to further interact with the digital assistant platform 108.).
For purposes of this rejection, the term “target content” is a known term in the field of natural language processing and digital assistant systems and is interpreted under broadest reasonable interpretation as the substantive information, or answer that satisfies the user’s request as determined by the model. While the claim language uses the term “target content,” Dhotre does not use the same terminology when referring to content that is responsive to the user’s query and that is output by the model for use in forming a reply, and instead uses “intent data.” However, this mere difference in terminology does not create a substantive distinction and serve the same function of representing the model’s determination of what the user is asking for so that the system can produce a responsive output.
Regarding claim 10, electronic device claim 10 and method claim 1 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 10 is similarly rejected under the same rationale as applied above with respect to method claim. Furthermore, Dhotre teaches:
at least one processing unit (see Column 13, 37-51, where the one or more processing units 1114 can include physical or virtual units that selectively execute software instructions, such as the software application instructions 1106. In an example, the one or more processing units 1114 can be physical products comprising one or more integrated circuits. The one or more processing units 1114 can be implemented as one or more processing cores. In another example, one or more processing units 1114 are implemented as one or more separate microprocessors. In yet another example embodiment, the one or more processing units 1114 can include an application-specific integrated circuit (ASIC) that provides specific functionality. In yet another example, the one or more processing units 1114 provide specific functionality by using an ASIC and by executing computer-executable instructions), and
at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit (see Column 12, lines 65-67, and Column 13, lines 1-16, where the memory 1104 can include a computer readable storage medium. The computer storage medium can be a device or article of manufacture that stores data and/or computer-executable instructions. The memory 1104 can include volatile and nonvolatile, transitory and non-transitory, removable and non-removable devices or articles of manufacture implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. By way of example, and not limitation, computer storage media may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), reduced latency DRAM, DDR2 SDRAM, DDR3 SDRAM, solid state memory, read-only memory (ROM), electrically-erasable programmable ROM, optical discs (e.g., CD-ROMs, DVDs, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.), magnetic tapes, and other types of devices and/or articles of manufacture that store data).
Regarding claim 19, non-transitory computer-readable storage medium claim 19 and method claim 1 are related as non-transitory computer-readable storage medium and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to method claim. Furthermore, Dhotre teaches:
a non-transitory computer-readable storage medium having a computer program stored thereon (see Column 12, lines 65-67, and Column 13, lines 1-16, where the memory 1104 can include a computer readable storage medium. The computer storage medium can be a device or article of manufacture that stores data and/or computer-executable instructions. The memory 1104 can include volatile and nonvolatile, transitory and non-transitory, removable and non-removable devices or articles of manufacture implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. By way of example, and not limitation, computer storage media may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), reduced latency DRAM, DDR2 SDRAM, DDR3 SDRAM, solid state memory, read-only memory (ROM), electrically-erasable programmable ROM, optical discs (e.g., CD-ROMs, DVDs, etc.), magnetic disks (e.g., hard disks, floppy disks, etc.), magnetic tapes, and other types of devices and/or articles of manufacture that store data).
Regarding claim(s) 2, 11, 20, Dhotre teaches:
wherein the processing result is represented in a second computer language (see Column 11, lines 22-27, where the example responsive content 810 can be structured in a standardized format, such that any responsive content received by the digital assistant platform 108 from any of the handlers 112a-x has a similar format, allowing the digital assistant platform 108 to receive and process responsive content in a standardized way), and
wherein presenting the reply message for the digital assistant for the conversation message comprises:
determining a first computer language supported by the interaction channel based on the first message presentation model (see Column 10, lines 1-11, where the digital assistant platform 108 can also adapt the content to fit the user device 102 (step 616). The digital assistant platform 108 can use the adapters 204 to adapt the content. For example, the adapters 204 can, if the user device 102 is a device without a display screen, adapt the content so that it provides voice-based responses and contains less information. As a further example, the adapters 204 can, if the user device is a device using a native mobile application, adapt the response to include both voice and text response, as well as a button for the user of the user device 102 to further interact with the digital assistant platform 108);
converting the processing result from the second computer language to description information in the first computer language (see Column 11, lines 44-49, where the digital assistant platform 108 can then adapt (e.g., at step 616), using the adapters 204, the example responsive content 810. For example, if the user device is a smart device, then the adapters 204 can adapt the example responsive content 810 to comply with communication protocols of the smart device); and
presenting the reply message based on the description information (see Column 11, lines 49-51, where the digital assistant platform 108 can then send (e.g., at step 618) the adapted responsive content to the user device).
For purposes of this rejection, the term “first computer language” is interpreted under broadest reasonable interpretation as the channel-specific presentation language or format that a given device type supports, which may include a domain-specific language (DSL) such as voice-based presentation language, or a text-based markup language. While the claim language uses the term “first computer language,” Dhotre does not use the same terminology when referring to the channel-specific presentation format that a device supports, and instead uses “adapted response content.” However, this mere difference in terminology does not create a substantive distinction and serve the same function of representing the channel-specific presentation format into which the processing result is converted to before being delivered to the user device.
Regarding claim 11, electronic device claim 11 and method claim 2 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 11 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim 20, non-transitory computer-readable storage medium claim 20 and method claim 2 are related as non-transitory computer-readable storage medium and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim(s) 5, and 14, Dhotre teaches:
wherein the digital assistant is capable of being triggered for interaction in a plurality of interaction channels (see Column 5, lines 10-30, where as illustrated in the example of FIG. 3, the user device 102 of FIG. 1 can be any of the devices 302-310. That is, the user device 102 can be a device using native website 302, a device using native app 304, a smart device 306, a device using chat bot 308, or another user device 310. The device using native website 302 can be, for example, a device that is using a website that is associated with the owner, creator, or affiliate of the digital assistant platform 108. The device using native app 304 can be, for example, a device that is using a mobile application that is associated with the owner, creator, or affiliate of the digital assistant platform 108. The smart device 306 can be, for example, Google Home, Amazon Alexa, Samsung Bixby, or any other smart device, including an Internet of things (IoT) device. The device using chat bot 308 can be any device, such as a computer or mobile phone, that is using a chat bot. The other user device 310 can be any device that can send a query, whether the query is a voice-based query or text-based query, to the digital assistant platform 108, via the network 104a. As illustrated by the other device 310, the user device 102 is not limited, in number or type, to devices 302-308), and
wherein the plurality of interaction channels has respective message presentation modes and support respective computer languages (see Column 5, lines 31-50, where the adapters 204 can include a plurality of adapters. In the example of FIG. 3, the adapters 204 include a native device adapter 312, a smart device adapter 314, a chat bot adapter 316, and another adapter 318. Each adapter of the adapters 204 can receive a query and standardize the query. Furthermore, each adapter of the adapters 204 can receive a different type of query, or a query from a different type of user device 102, and standardize the query. For example, the native device adapter 312 can receive and standardize a query sent from the device using native website 302 or the device using native app 304. The smart device adapter 314 can receive and standardize a query sent from the smart device 306. The chat bot adapter 316 can receive and standardize a query from the device using chat bot 308. The other adapter 318 can be any other adapter capable of receiving a query and standardizing the query. As illustrated by the other adapter 318, the adapters 204 are not limited, in number or type, to the adapters 312-316. Furthermore, the number of adapters included in the adapters 204 can be increased or decreased).
Regarding claim 14, electronic device claim 14 and method claim 5 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim(s) 6, and 15, Dhotre teaches wherein presenting the reply message of the digital assistant for the conversation message comprises:
determining, from the plurality of interaction channels, the interaction channel in which the conversation message is received (see Column 5, lines 51-64, where as illustrated in FIG. 3, and as further described below in connection with FIG. 6, the user device 102, whether it is the device using native website 302, the device using native app 304, the smart device 306, the device using chat bot 308, or the other user device 310, can send a query, via the network 104a, to the digital assistant platform 108. The query is received by the digital assistant platform 108 at the adapters 204. The adapters 204, whether it is the native device adapter 312, the smart device adapter 314, the chat bot adapter 316, or the other adapter 318, receive the query and standardize the query. After the adapters 204 standardize the query, the structure and form of the query are such, that its structure and form do not depend on the user device 102 that sends it); and
presenting, in the interaction channel, the reply message of the digital assistant for the conversation message based on the first message presentation mode corresponding to the determined interaction channel (see Column 10, lines 1-11, where the digital assistant platform 108 can also adapt the content to fit the user device 102 (step 616). The digital assistant platform 108 can use the adapters 204 to adapt the content. For example, the adapters 204 can, if the user device 102 is a device without a display screen, adapt the content so that it provides voice-based responses and contains less information. As a further example, the adapters 204 can, if the user device is a device using a native mobile application, adapt the response to include both voice and text response, as well as a button for the user of the user device 102 to further interact with the digital assistant platform 108).
Regarding claim 15, electronic device claim 15 and method claim 6 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim(s) 7, and 16, Dhotre teaches wherein the respective message presentation modes of the plurality of interaction channels are determined based on respective conversational user interface (CUI) capabilities of the plurality of interaction channels (see Column 10, lines 1-11, where the digital assistant platform 108 can also adapt the content to fit the user device 102 (step 616). The digital assistant platform 108 can use the adapters 204 to adapt the content. For example, the adapters 204 can, if the user device 102 is a device without a display screen, adapt the content so that it provides voice-based responses and contains less information. As a further example, the adapters 204 can, if the user device is a device using a native mobile application, adapt the response to include both voice and text response, as well as a button for the user of the user device 102 to further interact with the digital assistant platform 108. Also see Column 5, lines 32-35, where the adapters 204 include a native device adapter 312, a smart device adapter 314, a chat bot adapter 316, and another adapter 318. Each adapter of the adapters 204 can receive a query and standardize the query).
For purposes of this rejection, the term “interaction channel” is interpreted under broadest reasonable interpretation as a distinct mode, medium, or device-type through which the user can interact with the digital assistant. While the claim language uses the term “interaction channel,” Dhotre does not use the same terminology when referring to a distinct interaction pathway between user device and a digital assistant, and instead uses “user device type,” which may be a native device, a smart device, a chat bot app, etc. However, this mere difference in terminology does not create a substantive distinction and serve the same function of identifying the distinct pathway through which the user can interact with the digital assistant.
Regarding claim 16, electronic device claim 16 and method claim 7 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to method claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3-4, and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dhotre et al. (US 12524623 B2) in view of Deng et al. ("Image-to-Markup Generation with Coarse-to-Fine Attention", 2017).
Regarding claim 3, which depends on claim 2, Dhotre teaches all the limitations of claim 2 but fails to teach wherein the model is trained based on the second computer language.
However, Deng does teach wherein the model is trained based on the second computer language (see Page 1, Abstract, “We present a neural encoder-decoder model to convert images into presentational markup.” Also see Page 2, Section 2, “The target, y, consists of a sequence of tokens y1,y2,··· ,yT where T is the length of the output, and each y is a token in the markup language.” Page 3, Section 3, “The decoder implements a conditional language model over the vocabulary, and the whole model is trained to maximize the likelihood of the observed markup.” Also see Page 6, Section 6, “The complete model is trained end-to-end to maximize the likelihood of the training data. Beyond the training data, the model is given no other information about the markup language or the generating process.”
Dhotre and Deng are considered to be analogous to the claimed invention because they are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Dhotre to incorporate the teachings of Deng in order to output the second computer language being a markup language natively, thus reducing conversion errors regarding the second computer language and first computer language (see Page 1, Section 1, “We consider whether a supervised model can learn to produce correct presentational markup from an image, without requiring a textual or visual grammar of the underlying markup language.” Also see Page 2, Section 2, “The target, y, consists of a sequence of tokens y1,y2,··· ,yT where T is the length of the output, and each y is a token in the markup language.” Also see Page 3, Section 3, “The decoder implements a conditional language model over the vocabulary, and the whole model is trained to maximize the likelihood of the observed markup.”)
Regarding claim 12, electronic device claim 12 and method claim 3 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 12 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim 4, which depends on claim 2, Dhotre teaches all the limitations of claim 2 but fails to teach wherein the second computer language comprises a markup language.
However, Deng does teach wherein the second computer language comprises a markup language (see Figure 1, “The model generates one LaTeX symbol y at a time based on the input image x.” Also see Page 2, Section 2, “The target, y, consists of a sequence of tokens y1,y2,··· ,yT where T is the length of the output, and each y is a token in the markup language.”).
Regarding claim 13, electronic device claim 13 and method claim 4 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 13 is similarly rejected under the same rationale as applied above with respect to method claim.
Claim(s) 8-9, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dhotre et al. (US 12524623 B2) in view of Zhou (US 12124468 B1).
Regarding claim 8, which depends on claim 1, Dhotre teaches all the limitations of claim 1 but fails to teach presenting the target content in a statistical form, and presenting the reply in a determined presentation style and the target content based on the presentation style.
However, Zhou does teach:
wherein the processing result indicates presenting the target content in a target data statistical form (see Column 4, lines 22-27, where the digital assistant system of the present disclosure provides an integrated UI rendering solution within a digital assistant that leverages a LLM to generate explanatory text in view of and that is consistent with data that is displayed as a graphical representation (e.g., chart, graph). More particularly, the digital assistant system of the present disclosure leverages the ability of the LLM to generate code and an understanding of the data to automatically create a UI solution based on a user question and context), and
presenting the reply message of the digital assistant for the conversation message in the interaction channel comprises:
determining, in the first message presentation mode, a presentation style predefined for the target data statistical form (see Column 7, lines 60, and Column 8, lines 25, where Listing 5: Example UI Code shows a representation of a chart being predefined in a programming language based on the dataset. Also see Column 5, lines 20-22, where a graphical representation is generated by execution of the code 234 and is depicted in the window 224 of the popover container 222); and
presenting the target content in the reply message according to the presentation style (see Column 8, lines 34-38, where if the LLM response includes the code 234, the digital assistant 220 triggers opening of the popover container 222 with window 224 to execute the code 234 (UI code) and display the graphical representation of the data. Also see Column 9, lines 8-11, where explanatory text is displayed in the digital assistant (422) and the graphical representation is displayed in the popover container (424). For example, and as described herein, explanatory text is displayed in the digital assistant 220 and the popover container 222 is created and the code 234 is executed to display a graphical representation of the data within the popover container 222.).
Dhotre and Zhou are considered to be analogous to the claimed invention because they are in the same field of electrical data processing and natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to have modified Dhotre to incorporate the teachings of Zhou in order to (see Column 1, lines 31-38, where implementations of the present disclosure are directed to a digital assistant system that enables LLM-generated text to be generated based on enterprise data and presented to users within graphical user interface (GUIs) within digital assistants. In this manner, user-friendly and intuitive explanations of the data can be provided in hand with graphical representations of the data. Also see Column 3, lines 31-38, where in the field of artificial intelligence (AI), LLMs have gained increasing popularity. For example, LLMs can be used to generate user-friendly, textual explanations across a variety of topics, which can be presented in digital assistants (e.g., chatbots). However, functionality of LLMs is limited. For example, LLMs are not able to generate graphical representations of data (e.g., graphs, plots, charts) in the context of digital assistants).
Regarding claim 17, electronic device claim 17 and method claim 8 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to method claim.
Regarding claim 9, which depends on claim 8, Dhotre in view of Zhou teaches all the limitations in claim 8. Furthermore, Zhou teaches wherein the target data statistical form comprises at least one of the following: a chart type, or a form type (see Column 4, lines 22-27, where the digital assistant system of the present disclosure provides an integrated UI rendering solution within a digital assistant that leverages a LLM to generate explanatory text in view of and that is consistent with data that is displayed as a graphical representation (e.g., chart, graph). More particularly, the digital assistant system of the present disclosure leverages the ability of the LLM to generate code and an understanding of the data to automatically create a UI solution based on a user question and context).
Regarding claim 18, electronic device claim 18 and method claim 9 are related as electronic device and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to method claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Castillo et al. (U.S. PG Pub. 2025/0028759): User Interface Framework For Enhancing Content With Language Model Interactions
Gruber et al. (U.S. Patent 9318108): Intelligent Automated Assistant
Anisimov et al. (U.S. Patent 9553755): Method For Implementing And Executing Communication Center Routing Strategies Represented In Extensible Markup Language.
Li et al. (“MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding”, 11 Mar 2022)
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/JOHN HONG FANG-WU/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
10/01/2026
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