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
1. The instant application having Application No. 18/797,496 has claims 1-20 pending filed on 08/07/2024; there are 3 independent claims and 17 dependent claims, all of which are ready for examination by the examiner.
Response to Arguments
This Office Action is in response to applicant’s communication filed on November 10, 2025 in response to PTO Office Action dated August 28, 2025. The Applicant’s remarks and amendments to the claims and/or specification were considered with the results that follow.
Claim Rejections
Claim Rejections - 35 USC § 101
In view of the applicant’s amendments to the independent claims 1, 8 and 15 (dated 11/10/2025), the claim rejection under 35 U.S.C. § 101 for judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more of the claims 1-20 is withdrawn.
Claim Rejections - 35 USC § 103
35 USC § 103 Rejection of claims 1-20
Applicant's arguments filed on 11/10/2025 with respect to the claims 1-20 have been fully considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Acknowledgement Of References Cited By Applicant
As required by M.P.E.P. 609(C), the applicant’s submission of the Information Disclosure Statement dated January 26, 2026 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C (2), copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) ELEMENT IN CLAIM FOR A COMBINATION. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
The claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Such claim limitation(s) is/are: component in claims 8-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Objections
Claim Objections
a. The dependent claims 16-20 are objected to because of the following informality:
The Claims 16-20 recite inter alias “The method according to claim 15 … “ which incorrectly states the invention as method because the independent claim 15 states the invention as “non-transient computer-readable storage medium”. The applicant is requested to make the corrections to the wordings of the dependent claims 16-20.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a), as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent Claims 1, 8 and 15
As described above, the disclosure does not provide adequate structure to perform the claimed function or written description of the function “ … terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element … “. The support in the specifications for the amendments (specification Paragraph [0068] and Paragraph [0069] indicates “… The generation stopping instruction is used to display that the generation of the answer result for the questioning information is stopped, and meanwhile, the display of a new generated result content is also stopped … Since the server stops generating the answer result and the terminal also stops displaying any new generated result content, the terminal has stopped generating the answer result with respect to the user …”. There is no mention of “terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result”.
Dependent Claims 2-7, 9-14 and 16-20
The dependent claims 2-7, 9-14 and 16-20 are rejected under 35 U.S.C. 112(a) as they are dependent on independent claims 1, 8 and 15 directly or indirectly respectively and are thus rejected for the reasons specified supra for the independent claims 1, 8 and 15.
.
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.
Claims 1-4, 7-11 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Di Fabbrizio et al (US PGPUB 20230106590) in view of Xu et al (CN 108446321 A) and in further view of Hashimoto et al (US PGPUB 20200357405).
As per claim 1:
Di Fabbrizio teaches:
“A method for improving both efficiency and quality of information generation using a machine learning model, comprising” (Paragraph [0002], Paragraph [0021] and Paragraph [0035] (the method can also include determining an attribute of the first item by using a predictive model trained using question-answer pair data, the question-answer (QA) expansion system can provide query responses that can be generated accurately and more quickly than existing systems and where the QA expansion system includes a machine learning platform comprises))
“in response to receiving questioning information via an interface for interacting with the machine learning model” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which operates to receive query data, such as questions provided to the client device in regard to a particular item or product and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“initiating a process of generating an answer result by the machine learning model based on the questioning information” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which processes the query data to generate responses to the user in regard to the particular item or product that the user was inquiring about and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“displaying a portion of the answer result generated by the machine learning model on the interface during the process of generating the answer result by the machine learning model” (Paragraph [0002] and Paragraph [0034] (the method can include determining an attribute of the first item which can be performed using a predictive model trained using question-answer pair data associated with a portion of the plurality of items, the QA processing platform can generate a response to the user's query and the query response can be transmitted to the client device and provided as display output via output device))
Di Fabbrizio does not EXPLICITLY teach: receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon.
However, in an analogous art, Xu teaches:
receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface” (Page 2 Lines 51-52 (preferably, the server does not find the similarity satisfies the preset threshold value of the answer content, the method further comprises))
“displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result” (Page 3 Lines 14-16 and Page 3 Lines 43-45 (in the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface, specifically comprises: generating the display icon of the operation instruction in the intelligent terminal and when the intelligent terminal finishes the corresponding operation instruction, matching with the display icon corresponding to the operation instruction action))
“and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface” (Page 8 Lines 11-17 (corresponding to each trigger display interface in the corresponding operation after finishing the reading of the display interface content in the intelligent terminal, through the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface))
“wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon” (Page 5 Lines 53-57 and Page 8 Lines 19-22 (wherein the image recognition mode when obtaining the operation result corresponding to the display interface content may be matched with the question answer corresponding to the question content that is learned by the server history and the question and answer method is based on deep learning (machine learning) , especially for the learning part)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of XU and apply them on teachings of Di Fabbrizio for the method “receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon”. One would be motivated as the realization of automatic question and answer method based on deep learning, especially for the learning part, provides a high-efficiency feasible implementation means, reducing the cost of manual maintenance in the existing technology and the accuracy of the correct answer content corresponding to the content of the final matching problem is improved through the training process (Xu, Page 5 Lines 53-57).
Di Fabbrizio and Xu do not EXPLICITLY teach: terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element
However, in an analogous art, Hashimoto teaches:
“terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element” (Paragraph [0006] (an interaction stoppage unit that performs control for stopping the interaction by the interaction execution unit based on an interaction state by the user (in response to receiving a generation stopping instruction), and a response content provision unit that provides the response content according to a time of stoppage in a case where the control for stopping the interaction is performed by the interaction stoppage unit)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Hashimoto and apply them on teachings of Di Fabbrizio and Xu for the method “terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element”. One would be motivated as it is possible to perform the control for stopping the interaction based on the interaction state by the user and to stop the interaction early according to the interaction state, and thus it is possible to reduce the user from withdrawing in the middle of the search (Hashimoto, Paragraph [0007]).
As per claim 2:
Di Fabbrizio, Xu and Hashimto teach the method of claim 1 above.
Xu further teaches:
“wherein the answer result modification instruction is acquired according to the following steps” (Page 10 Lines 25-27 (the intelligent assistant generates the result data, the problem information will be determined according to the problem or key word input by the user, in combination with the interactive feedback data of the current context ))
“in response to the selection of the first icon of the plurality of icons, acquiring a target content question corresponding to the different type of issue corresponding to the first icon, and generating the answer result modification instruction according to the target content question” (Page 3 Lines 14-16, Page 3 Lines 43-45 and Page 7 Lines 55-57 ((the server receives the verification of the intelligent terminal by the response, that is, the user through the control of the intelligent terminal returns the selection operation of the confirmation to the server, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface, generating the display icon of the operation instruction in the intelligent terminal and when the intelligent terminal finishes the corresponding operation instruction, matching with the display icon corresponding to the operation instruction action)).
As per claim 3:
Di Fabbrizio, Xu and Hashimto teach the method of claim 1 above.
Di Fabbrizio further teaches:
“wherein the answer result modification instruction further comprises input content modification information, and wherein the answer result modification instruction is acquired according to the following steps” (Paragraph [0026] (the QA processing platform operates to receive query data, such as questions provided to the client device in regard to a particular item or product, and to process the query data to generate responses to the user that the user was inquiring includes))
“acquiring the input content modification information, and determining a question dimension and a target content question according to the content modification information and generating the answer result modification instruction according to the question dimension and the target content question ” (Paragraph [0041] (The QA processing platform 120 includes run-time components that are responsible for processing incoming speech or text inputs, determining the meaning in the context of a query and a product or item (according to the question dimension and the target content question), and generate query responses to the user which are provided as speech and/or text output)).
As per claim 4:
Di Fabbrizio, Xu and Hashimto teach the method of claim 2 above.
Xu further teaches:
“wherein the different type of issue corresponding to the first icon comprises a question understanding dimension” (Page 2 Lines 57-58 and Page 3 Line 43 (generating the display icon of the operation instruction in the intelligent terminal, matching the display icon corresponding to the operation and and controlling the intelligent terminal according to the question content (a question understanding dimension), triggering the corresponding operation instruction action in the display interface))
“the acquiring the target content question corresponding to the different type of issue comprises” (Page 2 Lines 57-58 and Page 3 Line 1 (reading the display interface content in the intelligent terminal, and controlling the intelligent terminal according to the question content, triggering the corresponding operation in the display interface one by one includes))
“displaying the questioning information in a dialog box of the intrface” (Page 3 Lines 14-16 (obtaining the display interface content corresponding to the operation result))
“acquiring edited updated questioning information in response to an editing operation for the questioning information in the dialog box” (Page 3 Lines 18-19 (through the image recognition mode, from the default keyword area in the display interface content, obtaining the operation result of the last level trigger instruction))
“and taking the updated questioning information as the target content question under the question understanding dimension” (Page 5 Lines 29-31 (obtaining the question content (target content question under the question understanding dimension) sent by the request terminal, and according to the semantic recognition model matched with the question content with the highest similarity of the answer content)).
As per claim 7:
Di Fabbrizio, Xu and Hashimto teach the method of claim 2 above.
Xu further teaches:
“wherein before displaying the updated answer result, the method further comprises” (Page 3 Lines 14-16 (the image recognition mode, obtaining the display interface content corresponding to the operation result))
“displaying preset answer information matched with the triggered content processing identifier below the displayed generated result content” (Page 5 Lines 29-31 (obtaining the question content sent by the request terminal, and according to the semantic recognition model matched with the question content with the highest similarity of the answer content, feeding back to the request terminal)).
As per claim 8:
Di Fabbrizio teaches:
“A computer device, comprising” (Paragraph [0023] (a large-format computing device or any other fully functional computing device includes))
“at least one processor and at least one memory” (Paragraoh [0024] (includes a processor and a memory))
“wherein the at least one memory stores machine-readable instructions executable by the at least one processor, and the at least one processor is used for executing the machine-readable instructions stored in the at least one memory” (Paragraph [0024] (the memory can store computer-readable instructions and/or data associated with processing multi-modal user data via a frontend and backend of the QA expansion system and the processor operates to execute the computer-readable instructions and/or data stored in memory))
“when the machine-readable instructions are executed by the at least one processor, the at least one processor executes” (Paragraph [0007] (the non-transitory computer program products (i.e., physically embodied computer program products including memory) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, causes at least one data processor to perform))
“a method for displaying information, which comprises” (Paragraph [0012] (a method for providing an answer to a query (display information) via the question-answer expansion system includes))
“in response to receiving questioning information via an interface for interacting with the machine learning model” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which operates to receive query data, such as questions provided to the client device in regard to a particular item or product and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“initiating a process of generating an answer result by the machine learning model based on the questioning information” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which processes the query data to generate responses to the user in regard to the particular item or product that the user was inquiring about and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“displaying a portion of the answer result generated by the machine learning model on the interface during the process of generating the answer result by the machine learning model” (Paragraph [0002] and Paragraph [0034] (the method can include determining an attribute of the first item which can be performed using a predictive model trained using question-answer pair data associated with a portion of the plurality of items, the QA processing platform can generate a response to the user's query and the query response can be transmitted to the client device and provided as display output via output device))
Di Fabbrizio does not EXPLICITLY teach: receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon.
However, in an analogous art, Xu teaches:
receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface” (Page 2 Lines 51-52 (preferably, the server does not find the similarity satisfies the preset threshold value of the answer content, the method further comprises))
“displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result” (Page 3 Lines 14-16 and Page 3 Lines 43-45 (in the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface, specifically comprises: generating the display icon of the operation instruction in the intelligent terminal and when the intelligent terminal finishes the corresponding operation instruction, matching with the display icon corresponding to the operation instruction action))
“and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface” (Page 8 Lines 11-17 (corresponding to each trigger display interface in the corresponding operation after finishing the reading of the display interface content in the intelligent terminal, through the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface))
“wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon” (Page 5 Lines 53-57 and Page 8 Lines 19-22 (wherein the image recognition mode when obtaining the operation result corresponding to the display interface content may be matched with the question answer corresponding to the question content that is learned by the server history and the question and answer method is based on deep learning (machine learning) , especially for the learning part)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of XU and apply them on teachings of Di Fabbrizio for the computer device “receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon”. One would be motivated as the realization of automatic question and answer method based on deep learning, especially for the learning part, provides a high-efficiency feasible implementation means, reducing the cost of manual maintenance in the existing technology and the accuracy of the correct answer content corresponding to the content of the final matching problem is improved through the training process (Xu, Page 5 Lines 53-57).
Di Fabbrizio and Xu do not EXPLICITLY teach: terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element
However, in an analogous art, Hashimoto teaches:
“terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element” (Paragraph [0006] (an interaction stoppage unit that performs control for stopping the interaction by the interaction execution unit based on an interaction state by the user (in response to receiving a generation stopping instruction), and a response content provision unit that provides the response content according to a time of stoppage in a case where the control for stopping the interaction is performed by the interaction stoppage unit)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Hashimoto and apply them on teachings of Di Fabbrizio and Xu for the computer device “terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element”. One would be motivated as it is possible to perform the control for stopping the interaction based on the interaction state by the user and to stop the interaction early according to the interaction state, and thus it is possible to reduce the user from withdrawing in the middle of the search (Hashimoto, Paragraph [0007]).
As per claim 9, the claim is rejected based upon the same rationale given for the parent claim 8 and the claim 2 above.
As per claim 10, the claim is rejected based upon the same rationale given for the parent claim 8 and the claim 3 above.
As per claim 11, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 4 above.
As per claim 14, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 7 above.
As per claim 15:
Di Fabbrizio teaches:
“A non-transient computer-readable storage medium” (Paragraph [0091] (a computer-readable medium described herein include))
“wherein the non-transient computer-readable storage medium has stored thereon a computer program, and when the computer program is run by a computer device, the computer device executes” (Paragraph [0007] (the non-transitory computer program products i.e., physically embodied computer program products are also described that store instructions, which when executed by one or more data processors of one or more computing systems, causes at least one data processor to perform operations))
“a method for displaying information, which comprises” (Paragraph [0012] (a method for providing an answer to a query (display information) via the question-answer expansion system includes))
“in response to receiving questioning information via an interface for interacting with the machine learning model” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which operates to receive query data, such as questions provided to the client device in regard to a particular item or product and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“initiating a process of generating an answer result by the machine learning model based on the questioning information” (Paragraph [0026] and Paragraph [0035] (the QA expansion system includes a QA processing platform which processes the query data to generate responses to the user in regard to the particular item or product that the user was inquiring about and the QA expansion system includes a machine learning platform which can refer to an application of artificial intelligence that automates the development of an analytical model))
“displaying a portion of the answer result generated by the machine learning model on the interface during the process of generating the answer result by the machine learning model” (Paragraph [0002] and Paragraph [0034] (the method can include determining an attribute of the first item which can be performed using a predictive model trained using question-answer pair data associated with a portion of the plurality of items, the QA processing platform can generate a response to the user's query and the query response can be transmitted to the client device and provided as display output via output device))
Di Fabbrizio does not EXPLICITLY teach: receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon.
However, in an analogous art, Xu teaches:
receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface” (Page 2 Lines 51-52 (preferably, the server does not find the similarity satisfies the preset threshold value of the answer content, the method further comprises))
“displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result” (Page 3 Lines 14-16 and Page 3 Lines 43-45 (in the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface, specifically comprises: generating the display icon of the operation instruction in the intelligent terminal and when the intelligent terminal finishes the corresponding operation instruction, matching with the display icon corresponding to the operation instruction action))
“and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface” (Page 8 Lines 11-17 (corresponding to each trigger display interface in the corresponding operation after finishing the reading of the display interface content in the intelligent terminal, through the image recognition mode, obtaining the display interface content corresponding to the operation result, so as to generate trigger instruction of the corresponding operation in the next display interface))
“wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon” (Page 5 Lines 53-57 and Page 8 Lines 19-22 (wherein the image recognition mode when obtaining the operation result corresponding to the display interface content may be matched with the question answer corresponding to the question content that is learned by the server history and the question and answer method is based on deep learning (machine learning) , especially for the learning part)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of XU and apply them on teachings of Di Fabbrizio for the non-transient computer-readable storage medium “receiving a selection of a user interface element displayed on the interface while the portion of the answer result is displayed on the interface; displaying a plurality of icons on the interface in response to terminating the process of generating the answer result by the machine learning model, wherein each of the plurality of icons corresponds to a different type of issue associated with the portion of the answer result; and in response to receiving an answer result modification instruction comprising a selection of a first icon of the plurality of icons, displaying an updated answer result on the interface; wherein the updated answer result is generated by the machine learning model by modifying the portion of the answer result to resolve a type of issue corresponding to the first icon”. One would be motivated as the realization of automatic question and answer method based on deep learning, especially for the learning part, provides a high-efficiency feasible implementation means, reducing the cost of manual maintenance in the existing technology and the accuracy of the correct answer content corresponding to the content of the final matching problem is improved through the training process (Xu, Page 5 Lines 53-57).
Di Fabbrizio and Xu do not EXPLICITLY teach: terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element
However, in an analogous art, Hashimoto teaches:
“terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element” (Paragraph [0006] (an interaction stoppage unit that performs control for stopping the interaction by the interaction execution unit based on an interaction state by the user (in response to receiving a generation stopping instruction), and a response content provision unit that provides the response content according to a time of stoppage in a case where the control for stopping the interaction is performed by the interaction stoppage unit)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Hashimoto and apply them on teachings of Di Fabbrizio and Xu for the non-transient computer-readable storage medium “terminating the process of generating the answer result by the machine learning model without generating a remainder of the answer result in response to receiving the selection of the user interface element”. One would be motivated as it is possible to perform the control for stopping the interaction based on the interaction state by the user and to stop the interaction early according to the interaction state, and thus it is possible to reduce the user from withdrawing in the middle of the search (Hashimoto, Paragraph [0007]).
As per claim 16, the claim is rejected based upon the same rationale given for the parent claim 15 and the claim 2 above.
As per claim 17, the claim is rejected based upon the same rationale given for the parent claim 15 and the claim 3 above.
As per claim 18, the claim is rejected based upon the same rationale given for the parent claim 16 and the claim 4 above.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Di Fabbrizio et al (US PGPUB 20230106590) in view of Xu et al (CN 108446321 A) and in further view of Hashimoto et al (US PGPUB 20200357405) and Wang et al (CN 117037553 A).
As per claim 5:
Di Fabbrizio, Xu and Hashimto teach the method of claim 2 above.
Xu further teaches:
“wherein the different type of issue corresponding to the first icon comprises” (Page 3 Lines 43-45 (wherein generating the display icon of the operation instruction in the intelligent terminal comprises))
“the acquiring the target content question corresponding to the different type of issue comprises” (Page 2 Lines 23-24 (obtaining the question content sent by the request terminal, and matching the answer content with the highest similarity with the question content comprises)).
Di Fabbrizio, Xu and Hashimto do not EXPLICITLY teach: comprises a content error; acquiring input error content information according to first error input prompt information in the dialog box of the interface; determining the target content question according to the error content information and the content error dimension.
However, in an analogous art, Wang teaches:
“comprises a content error” (Page 2 Lines 36-40 (the processing result comprises the first information representing that there is an error in the answering content))
“acquiring input error content information according to first error input prompt information in the dialog box of the interface” (Page 3 Lines 39-40 (if the processing result comprises the first information representing that there is an error in the answering content, the target topic is the topic displayed by the auxiliary learning system, and the answering content is the content input by the user for the target topic))
“determining the target content question according to the error content information and the content error dimension” (Page 8 Lines 25-30 (the second information includes an error type of the solution step in which there is an error and by performing the step-by-step correction on the answer content, helping the user to quickly locate the error position, by outputting the error type of the answer step in which there is an error)).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Wang and apply them on teachings of Di Fabbrizio, Xu and Hashimto for the method “: comprises a content error; acquiring input error content information according to first error input prompt information in the dialog box of the interface; determining the target content question according to the error content information and the content error dimension”. One would be motivated as it helps the user to modify the error of the answering content with pertinence, improves the auxiliary performance of the user learning, and further improves the intelligence of the auxiliary learning (Wang, Page 5 Lines 2-4).
As per claim 12, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 5 above.
As per claim 19, the claim is rejected based upon the same rationale given for the parent claim 16 and the claim 5 above.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Di Fabbrizio et al (US PGPUB 20230106590) in view of Xu et al (CN 108446321 A) and in further view of Hashimoto et al (US PGPUB 20200357405) and Liao et al (CN 109388697 A).
As per claim 6:
Di Fabbrizio, Xu and Hashimto teach the method of claim 2 above.
Xu further teaches:
“wherein the different type of issue corresponding to the first icon comprises” (Page 3 Lines 43-45 (wherein generating the display icon of the operation instruction in the intelligent terminal comprises)) “the displaying an updated answer result comprises” (Page 6 Lines 21 (displaying in association with an answer))
“the acquiring the target content question corresponding to the different type of issue comprises” (Page 2 Lines 23-24 (obtaining the question content sent by the request terminal, and matching the answer content with the highest similarity with the question content comprises))
“the displaying an updated answer result comprises” (Page 2 Lines 24-25 (matching the answer content with the highest similarity with the question content and feeding back answer result to the request terminal)).
“displaying a default answer result, the default answer result being pre-generated for the content redundancy question” (Page 5 Lines 29-31 (obtaining the question content sent by the request terminal, and according to the semantic recognition model matched with the question content with the highest similarity of the answer content (the default answer result being pre-generated), feeding back to the request terminal))
“and displaying the portion of the answer in the updated answer result according to a generation progress of the updated answer result,” (Page 8 Lines 11-17 (through the image recognition mode, obtaining the display interface content corresponding to the operation result (the portion of the answer in the updated answer result))).
Di Fabbrizio, Xu and Hashimto do not EXPLICITLY teach: comprises a content redundancy dimension; acquiring a content redundancy question under the content redundancy dimension; and taking the content redundancy question as the target content question under the content redundancy dimension; the portion of the result in the updated answer result being obtained by performing content simplification on the answer result according to the content redundancy question.
However, in an analogous art, Liao teaches:
“comprises a content redundancy dimension” (Page 9 Lines 11-16 (by said structure first question-answer pair method, on one hand, can obtain a plurality of answers for the same problem (a content redundancy dimension for the answer content)))
“acquiring a content redundancy question under the content redundancy dimension” (Page 9 Lines 20-22 (identifying the first question in the answer, of the redundancy information))
“and taking the content redundancy question as the target content question under the content redundancy dimension” (Page 8 Lines 26-27 and Page 9 Lines 20-22 (identifying the first question in the answer, of the redundancy information where the redundancy information comprises link, expression pattern, at least one of the special symbols and a target session segment is obtained from the target conversation segment to obtain a first question-answer pair))
“the portion of the result in the updated answer result being obtained by performing content simplification on the answer result according to the content redundancy question” (Page 16 Lines 34-38 (the identifying module is used for identifying the first question in the answer, of the redundancy information in the redundancy information comprises link, expression pattern, at least one of the special symbols, the unified processing module is used for transmitting the redundancy information to be deleted is identified or uniform replacement (performing content simplification on the answer result))).
It would have been obvious to one of ordinary skill in the art before the effective filing date to take the teachings of Liao and apply them on teachings of Di Fabbrizio, Xu and Hashimto for the method “comprises a content redundancy dimension; acquiring a content redundancy question under the content redundancy dimension; and taking the content redundancy question as the target content question under the content redundancy dimension; the portion of the result in the updated answer result being obtained by performing content simplification on the answer result according to the content redundancy question”. One would be motivated as the redundancy information to be deleted is identified or uniform replacement in question to obtain the pre-processed method for pre-processing a plurality of, any question or answer (Liao, Page 9 Lines 24-27).
As per claim 13, the claim is rejected based upon the same rationale given for the parent claim 9 and the claim 6 above.
As per claim 20, the claim is rejected based upon the same rationale given for the parent claim 16 and the claim 6 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hebenthal et al, (US PGPUB 20200320254), system and method for dynamically displaying a user interface of an evaluation system configured to evaluate predicted answers generated by a machine learning system. For example, the method includes receiving textual data and a predicted answer to a question associated with a text object. The text object includes a structured data field of the textual data. The predicted answer includes a confidence level. The confidence level is determined by a machine learning system based at least in part on one or more models of the machine learning system and the textual data.
Huang et al, (CN114327731A), the invention claims an information display method, device, device and medium. wherein the information display method comprises: responding to the trigger of the session answering event, displaying the answer selection control, the answer selection control comprises a selected mark and a plurality of selectors, each selector comprises a plurality of option display positions, each option display position is used for displaying an answer element, the selected mark is used for marking the answer factor displayed in one option display position in each selector as the selected state; receiving the first trigger operation of the answer selection control, the first trigger operation is used for changing the position of each option display position in at least one selector, so as to change the answer element marked by the selected mark; displaying the answer information, the answer information is generated according to the target answer element; the target answer element comprises the answer element marked by the selected mark at the end of the first trigger operation.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/KD/
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163