DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/09/2026 has been entered.
Accordingly, claims 1, 4-9, 12-13, 16, and 19-20 are pending in this application. Claims 1, 8, 13, and 20 are currently amended. Claims 10, 11, 17 and 18 have been canceled.
Response to Arguments
Applicant’s arguments with respect to amended pending claims filed on 3/09/2026 have been fully considered. In view of the claim amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
Further, regarding the new limitations recited in claims 1, 8, 13, and 20, it is submitted that they are properly addressed by the new ground of rejection.
Furthermore, it is also submitted that all limitations in pending claims, including those not specifically argued, are properly addressed. The reason is set forth in the rejections. See claim analysis below for detail.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-9, 12-13, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chockalingam (US 20240386037 A1) in view of He (US 20160135021 A1).
Regarding Claim 1, Chockalingam discloses a human-machine interaction method, comprising:
acquiring a question input by a user during a conversation with a large language model ([0025]-[0026]: The queries and replies may form a conversation… Referring to FIG. 4, second portion 42 may include a query 42b (as entered by the user), and a corresponding reply 42c (provided by large language model 133). As depicted, query 42b is a question);
retrieving memory information in a memory bank, the memory information being historical memory information about the user (Fig. 4; [0025]-[0026]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time… Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents; Fig. 1; [0041]: Server(s) 102 may include electronic storage 122);
wherein the memory bank comprises a long-term memory bank and a short-term memory bank (Fig. 1; [0042]: Electronic storage 122 may comprise non-transitory storage media that electronically stores information);
the long-term memory bank comprises a user memory bank persistently storing user portrait attribute information of the user in a structured form (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); and
the short-term memory bank comprises a conversation memory bank storing historical conversation information between the user and the large language model in a vector form within recent predetermined duration (Fig. 1, electronic storage 122; Fig. 4, [0025]: In some implementations, first portion 41 may be configured to select an individual conversation from a set of (on-going) conversations (which may also be referred to as “projects” or “sessions” in some cases). For example, as depicted, a conversation 41a, labeled “C1”, is currently selected; [0017]: By way of non-limiting example, the electronic formats of the electronic documents may be one or more of Portable Document Format… and/or other formats);
wherein the user portrait attribute information comprises user portrait attribute information generated according to one or both of historical conversation information between the user and the large language model and user information of the user collected from a predetermined data source (Fig. 1; [0021]-[0022]: In some implementations, particular electronic source document 123 may represent one or more of a bank statement, a financial record, a photocopy of a physical document from a government agency, and/or other documents… an account holder's name and address as indicated by an indicator 32, an overview of checking account information);
in response to retrieved memory information from at least one of the user memory bank and the conversation memory bank required for generating answer information corresponding to the question (Fig. 4; corresponding reply 42c), taking the retrieved memory information as matched memory information, and generating the answer information by the large language model in conjunction with the matched memory information ([0026]-[0030]: This question has been provided as a prompt (or input) to large language model 133. Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents),
storing the conversation information generated between the user and the large language model in the conversation memory bank in real time (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); Fig. 1; [0042]: Electronic storage 122 may store software algorithms, information determined by processor(s) 124, information received from server(s) 102, information received from client computing platform(s) 104, and/or other information that enables server(s) 102 to function as described herein)
However, Chockalingam does not explicitly teach “determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored, extracting key information from the preset number of new round conversations, and replacing the preset number of new round conversations with the extracted key information; and determining that a memory conversion condition is met at a preset time and determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated, updating the user portrait attribute information according to the memory information in the conversation memory bank.”
On the other hand, in the same field of endeavor, He teaches
determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored (Fig. 2; [0009]: According to a first aspect, a conversation merging method is provided, where the method is applied to a mobile terminal, and the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule),
extracting key information from the preset number of new round conversations (Fig. 2; [0009]-[0012]: the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule… extracting a number list of the second conversation according to the reply instruction; [0091]: The default name may be a conversation name that is… extracted by a terminal according to content of the conversation (which may be a part of the content of the conversation)), and
replacing the preset number of new round conversations with the extracted key information (Fig. 2; [0009]: merging the M first conversations into a second conversation); and
determining that a memory conversion condition is met at a preset time ([0010]- [0011]: the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode… the preset merging rule is a preset number set; and that M numbers meet a preset merging rule) and
determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated [0010]: With reference to the first aspect, in a first possible implementation manner of the first aspect, the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode; [0144]: The merging rule table in the merging setting main interface may be presented after the correspondence stored in the memory is read),
updating the user portrait attribute information according to the memory information in the conversation memory bank (Fig. 2; [0066]: Step 1112 is merging the M first IM conversations into a second IM conversation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chockalingam to incorporate the teachings of He to include updating the user information according to the memory information in the conversation memory bank.
The motivation for doing so would be to consolidate dispersed information in multiple conversation, as recognized by He ([0007] of He: In this case, for a user, different numbers that belong to a same sender are displayed in multiple conversations, which causes dispersed information and complex display of a conversation).
Regarding Claim 4, the combined teachings of Chockalingam and He disclose the method according to claim 1.
Chockalingam further teaches wherein the user memory bank also comprises at least one of memory information about identity setting or a personalized requirement of the user stored actively by the user (Fig. 4; [0026]: Note that reply 42f has been formatted for presentation as requested. By way of non-limiting example, queries may request formatting and/or rendering in different styles, as different objects).
Regarding Claim 5, the combined teachings of Chockalingam and He disclose the method according to claim 1.
Chockalingam further teaches wherein the long-term memory bank further comprises a system memory bank (Fig. 1, electronic storage 122);
the system memory bank comprises one or any combination of the following memory information: identity setting information of the large language model, background knowledge information of the large language model and reference document information of the large language model (Fig. 3A; [0003]: The system may… select one or more documents to be provided as input to a large language model for an individual conversation; [0022]: Exemplary electronic source document 30 includes many content blocks representing human-readable information, including various familiar elements for a bank statement, such as, by way of non-limiting example, the bank's name, address, and logo of the bank as indicated by an indicator 31).
Regarding Claim 6, the combined teachings of Chockalingam and He disclose the method according to claim 5.
Chockalingam further teaches wherein the memory information in the system memory bank is stored in an unstructured form (Fig. 1; [0021]-[0023]: For example, a particular electronic source document 123 may include a captured and/or generated image and/or video… a text-based document format).
Regarding Claim 7, the combined teachings of Chockalingam and He disclose the method according to claim 1.
Chockalingam further teaches wherein the user portrait attribute information in the user memory bank is stored in a key-value pair form ([0023]: As depicted, column 36 is part of a table 37. For example, the set of characters “Beginning Balance” form a row label, the set of characters “AMOUNT” form a column label, and the set of characters “$1000.00” form the attribute value for this row).
Regarding Claim 8, the combined teachings of Chockalingam and He disclose the method according to claim 4.
Chockalingam further teaches wherein the memory information actively stored by the user in the user memory bank are stored in a vector form ([0017]: By way of non-limiting example, the electronic formats of the electronic documents may be one or more of Portable Document Format (PDF), Portable Network Graphics (PNG), Tagged Image File Format (TIF or TIFF), Joint Photographic Experts Group (JPG or JPEG), and/or other formats [A vector form corresponds to other formats]).
Additionally, He teaches ([0153]: It should be noted that this embodiment of the present disclosure imposes no limitation on a storage form of a conversation in an information base).
Regarding Claim 9, the combined teachings of Chockalingam and He disclose the method according to claim 5.
Chockalingam further teaches further comprising: in response to acquiring an operation instruction issued by the user for any memory bank, completing a corresponding memory bank operation according to the operation instruction, the memory bank operation comprising addition of new memory information, deletion of existing memory information and modification of the existing memory information ([0025]: In some implementations, second portion 42 may include a notification 42a regarding modifications (e.g., additions or deletions of documents) of the current corpus of electronic documents).
Regarding Claim 12, the combined teachings of Chockalingam and He disclose the method according to claim 1.
Chockalingam further teaches wherein generating the answer information by the large language model in conjunction with the matched memory information comprises:
in response to acquiring the matched memory information from only one memory bank, converting the matched memory information into a predetermined format to obtain a first conversion result ([0023]: In some implementations, exemplary document 35 may have been created from exemplary electronic source document 30 in FIG. 3A, by converting that document to a text-based document format), and generating the answer information using the large language model in conjunction with the first conversion result (Fig. 1; [0024]: The one or more documents may be provided as input to large language model 133 for a particular conversation between the particular user and the one or more documents); and
in response to acquiring the matched memory information from at least two different memory banks, fusing the matched memory information acquired from the different memory banks ([0023]: The sets of characters in exemplary document 35 may correspond to content blocks in exemplary electronic source document 30 in FIG. 3A. For example, a column 36 of right-aligned sets of characters (in this case, numerical information such as amounts of currency) may correspond to certain content blocks in exemplary electronic source document 30 in FIG. 3A),
converting the fusion result into a predetermined format to obtain a second conversion result, and generating the answer information using the large language model in conjunction with the second conversion result ([0023]: For example, the set of characters “Beginning Balance” form a row label, the set of characters “AMOUNT” form a column label, and the set of characters “$1000.00” form the attribute value for this row. Likewise, in table 37, “$840.00” is the attribute value (or amount) for “Ending Balance”).
Regarding Claim 13, Chockalingam discloses an electronic device, comprising: at least one processor; and a memory connected with the at least one processor communicatively; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a human-machine interaction method ([0038]: A given client computing platform 104 may include one or more processors configured to execute computer program components), comprising:
acquiring a question input by a user during a conversation with a large language model ([0025]-[0026]: The queries and replies may form a conversation… Referring to FIG. 4, second portion 42 may include a query 42b (as entered by the user), and a corresponding reply 42c (provided by large language model 133). As depicted, query 42b is a question);
retrieving memory information in a memory bank, the memory information being historical memory information about the user (Fig. 4; [0025]-[0026]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time… Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents; Fig. 1; [0041]: Server(s) 102 may include electronic storage 122);
wherein the memory bank comprises a long-term memory bank and a short-term memory bank (Fig. 1; [0042]: Electronic storage 122 may comprise non-transitory storage media that electronically stores information);
the long-term memory bank comprises a user memory bank storing persistently user portrait attribute information of the user in a structured form (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); and
the short-term memory bank comprises a conversation memory bank storing historical conversation information between the user and the large language model in a vector form within recent predetermined duration (Fig. 1, electronic storage 122; Fig. 4, [0025]: In some implementations, first portion 41 may be configured to select an individual conversation from a set of (on-going) conversations (which may also be referred to as “projects” or “sessions” in some cases). For example, as depicted, a conversation 41a, labeled “C1”, is currently selected; [0017]: By way of non-limiting example, the electronic formats of the electronic documents may be one or more of Portable Document Format… and/or other formats);
wherein the user portrait attribute information comprises user portrait attribute information generated according to one or both of historical conversation information between the user and the large language model and user information of the user collected from a predetermined data source (Fig. 1; [0021]-[0022]: In some implementations, particular electronic source document 123 may represent one or more of a bank statement, a financial record, a photocopy of a physical document from a government agency, and/or other documents… an account holder's name and address as indicated by an indicator 32, an overview of checking account information);
in response to retrieved memory information from at least one of the user memory bank and the conversation memory bank required for generating answer information corresponding to the question (Fig. 4; corresponding reply 42c), taking the retrieved memory information as matched memory information, and generating the answer information by the large language model in conjunction with the matched memory information ([0026]-[0030]: This question has been provided as a prompt (or input) to large language model 133. Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents),
storing the conversation information generated between the user and the large language model in the conversation memory bank in real time (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); Fig. 1; [0042]: Electronic storage 122 may store software algorithms, information determined by processor(s) 124, information received from server(s) 102, information received from client computing platform(s) 104, and/or other information that enables server(s) 102 to function as described herein)
However, Chockalingam does not explicitly teach “determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored, extracting key information from the preset number of new round conversations, and replacing the preset number of new round conversations with the extracted key information; and determining that a memory conversion condition is met at a preset time and determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated, updating the user portrait attribute information according to the memory information in the conversation memory bank.”
On the other hand, in the same field of endeavor, He teaches
determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored (Fig. 2; [0009]: According to a first aspect, a conversation merging method is provided, where the method is applied to a mobile terminal, and the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule),
extracting key information from the preset number of new round conversations (Fig. 2; [0009]-[0012]: the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule… extracting a number list of the second conversation according to the reply instruction; [0091]: The default name may be a conversation name that is… extracted by a terminal according to content of the conversation (which may be a part of the content of the conversation)), and
replacing the preset number of new round conversations with the extracted key information (Fig. 2; [0009]: merging the M first conversations into a second conversation); and
determining that a memory conversion condition is met at a preset time ([0010]- [0011]: the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode… the preset merging rule is a preset number set; and that M numbers meet a preset merging rule) and
determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated [0010]: With reference to the first aspect, in a first possible implementation manner of the first aspect, the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode; [0144]: The merging rule table in the merging setting main interface may be presented after the correspondence stored in the memory is read),
updating the user portrait attribute information according to the memory information in the conversation memory bank (Fig. 2; [0066]: Step 1112 is merging the M first IM conversations into a second IM conversation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chockalingam to incorporate the teachings of He to include updating the user information according to the memory information in the conversation memory bank.
The motivation for doing so would be to consolidate dispersed information in multiple conversation, as recognized by He ([0007] of He: In this case, for a user, different numbers that belong to a same sender are displayed in multiple conversations, which causes dispersed information and complex display of a conversation).
Regarding Claim 16, the combined teachings of Chockalingam and He disclose the electronic device according to claim 13.
Chockalingam further teaches wherein the long-term memory bank further comprises a system memory bank (Fig. 1, electronic storage 122);
the system memory bank comprises one or any combination of the following memory information: identity setting information of the large language model, background knowledge information of the large language model and reference document information of the large language model (Fig. 3A; [0003]: The system may… select one or more documents to be provided as input to a large language model for an individual conversation; [0022]: Exemplary electronic source document 30 includes many content blocks representing human-readable information, including various familiar elements for a bank statement, such as, by way of non-limiting example, the bank's name, address, and logo of the bank as indicated by an indicator 31).
Regarding Claim 19, the combined teachings of Chockalingam and He disclose the electronic device according to claim 13.
Chockalingam further teaches wherein generating the answer information by the large language model in conjunction with the matched memory information comprises: in response to acquiring the matched memory information from only one memory bank, converting the matched memory information into a predetermined format to obtain a first conversion result ([0023]: In some implementations, exemplary document 35 may have been created from exemplary electronic source document 30 in FIG. 3A, by converting that document to a text-based document format), and generating the answer information using the large language model in conjunction with the first conversion result (Fig. 1; [0024]: The one or more documents may be provided as input to large language model 133 for a particular conversation between the particular user and the one or more documents); and
in response to acquiring the matched memory information from at least two different memory banks, fusing the matched memory information acquired from the different memory banks ([0023]: The sets of characters in exemplary document 35 may correspond to content blocks in exemplary electronic source document 30 in FIG. 3A. For example, a column 36 of right-aligned sets of characters (in this case, numerical information such as amounts of currency) may correspond to certain content blocks in exemplary electronic source document 30 in FIG. 3A), converting the fusion result into a predetermined format to obtain a second conversion result, and generating the answer information using the large language model in conjunction with the second conversion result ([0023]: For example, the set of characters “Beginning Balance” form a row label, the set of characters “AMOUNT” form a column label, and the set of characters “$1000.00” form the attribute value for this row. Likewise, in table 37, “$840.00” is the attribute value (or amount) for “Ending Balance”).
Regarding Claim 20, Chockalingam discloses a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a human-machine interaction method ([0042]: Electronic storage 122 may comprise non-transitory storage media that electronically stores information), comprising:
acquiring a question input by a user during a conversation with a large language model ([0025]-[0026]: The queries and replies may form a conversation… Referring to FIG. 4, second portion 42 may include a query 42b (as entered by the user), and a corresponding reply 42c (provided by large language model 133). As depicted, query 42b is a question);
retrieving memory information in a memory bank, the memory information being historical memory information about the user (Fig. 4; [0025]-[0026]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time… Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents; Fig. 1; [0041]: Server(s) 102 may include electronic storage 122);
wherein the memory bank comprises a long-term memory bank and a short-term memory bank (Fig. 1; [0042]: Electronic storage 122 may comprise non-transitory storage media that electronically stores information);
the long-term memory bank comprises a user memory bank persistently storing user portrait attribute information of the user in a structured form (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); and
the short-term memory bank comprises a conversation memory bank storing historical conversation information between the user and the large language model in a vector form within recent predetermined duration (Fig. 1, electronic storage 122; Fig. 4, [0025]: In some implementations, first portion 41 may be configured to select an individual conversation from a set of (on-going) conversations (which may also be referred to as “projects” or “sessions” in some cases). For example, as depicted, a conversation 41a, labeled “C1”, is currently selected; [0017]: By way of non-limiting example, the electronic formats of the electronic documents may be one or more of Portable Document Format… and/or other formats);
wherein the user portrait attribute information comprises user portrait attribute information generated according to one or both of historical conversation information between the user and the large language model and user information of the user collected from a predetermined data source (Fig. 1; [0021]-[0022]: In some implementations, particular electronic source document 123 may represent one or more of a bank statement, a financial record, a photocopy of a physical document from a government agency, and/or other documents… an account holder's name and address as indicated by an indicator 32, an overview of checking account information);
in response to retrieved memory information from at least one of the user memory bank and the conversation memory bank required for generating answer information corresponding to the question (Fig. 4; corresponding reply 42c), taking the retrieved memory information as matched memory information, and generating the answer information by the large language model in conjunction with the matched memory information ([0026]-[0030]: This question has been provided as a prompt (or input) to large language model 133. Reply 42c is “John Johnson”, which is retrieved from (or based on) multiple documents),
storing the conversation information generated between the user and the large language model in the conversation memory bank in real time (Fig. 4; [0025]: Second portion 42 may reflect at least a portion of a particular conversation history (also referred to as “chat history”, which may be persistent over time, so a particular user can come back to a conversation at some point in the future and continue the conversation); Fig. 1; [0042]: Electronic storage 122 may store software algorithms, information determined by processor(s) 124, information received from server(s) 102, information received from client computing platform(s) 104, and/or other information that enables server(s) 102 to function as described herein)
However, Chockalingam does not explicitly teach “determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored, extracting key information from the preset number of new round conversations, and replacing the preset number of new round conversations with the extracted key information; and determining that a memory conversion condition is met at a preset time and determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated, updating the user portrait attribute information according to the memory information in the conversation memory bank.”
On the other hand, in the same field of endeavor, He teaches
determining that the stored conversation information meets an extraction condition once a preset number of new round conversations have been stored (Fig. 2; [0009]: According to a first aspect, a conversation merging method is provided, where the method is applied to a mobile terminal, and the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule),
extracting key information from the preset number of new round conversations (Fig. 2; [0009]-[0012]: the method includes acquiring M first conversations, where M numbers in one-to-one correspondence with the M first conversations meet a preset merging rule… extracting a number list of the second conversation according to the reply instruction; [0091]: The default name may be a conversation name that is… extracted by a terminal according to content of the conversation (which may be a part of the content of the conversation)), and
replacing the preset number of new round conversations with the extracted key information (Fig. 2; [0009]: merging the M first conversations into a second conversation); and
determining that a memory conversion condition is met at a preset time ([0010]- [0011]: the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode… the preset merging rule is a preset number set; and that M numbers meet a preset merging rule) and
determining, according to the memory information in the conversation memory bank, that the user portrait attribute information in the user memory bank is required to be updated [0010]: With reference to the first aspect, in a first possible implementation manner of the first aspect, the preset merging rule includes a merging mode and a merging condition corresponding to the merging mode; [0144]: The merging rule table in the merging setting main interface may be presented after the correspondence stored in the memory is read),
updating the user portrait attribute information according to the memory information in the conversation memory bank (Fig. 2; [0066]: Step 1112 is merging the M first IM conversations into a second IM conversation).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teaching of Chockalingam to incorporate the teachings of He to include updating the user information according to the memory information in the conversation memory bank.
The motivation for doing so would be to consolidate dispersed information in multiple conversation, as recognized by He ([0007] of He: In this case, for a user, different numbers that belong to a same sender are displayed in multiple conversations, which causes dispersed information and complex display of a conversation).
Conclusion
20. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY D. HICKS whose telephone number is (571)272-3304. The examiner can normally be reached Mon - Fri 7:30 - 4:00.
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/S.D.H./Examiner, Art Unit 2168
/CHARLES RONES/Supervisory Patent Examiner, Art Unit 2168
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