DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/16/2025 has been entered.
Claims 1-4, 8-11, 15-17, and 21-29 remain pending in this application. Claims 1, 8, 11, 15, and 17 have been amended. Claims 1, 8, and 15 are independent claims.
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 .
Claim Objections
Claims 1, 8, and 15 are objected to because of the following informalities:
For each of the independent claims, replace … determine/determining the first chat bot response …. based on the user profile, and a chat bot personality … with … determine/determining the first chat bot response … based on the user profile[[,]] and a chat bot personality …
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.
Claims 1-4, 8-11, 15-17, and 21-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 8, and 15 all recite “iteratively predicting the user personality, based on the set of training data, the set of training data selected from data within users profiles, the users profiles comprising users sentiment logs, users preferences, and users chat histories”, “testing and comparing the user personality predicted during each iteration against a target variable”, “determine/determining a user personality based on data within the user profile … wherein the user profile comprises: a user sentiment log, user preferences, ….”, “determine/determining the first chat bot response, wherein the first chat bot response comprises …”, and “determine/determining a first user sentiment based on the first user reply” all of which can be recognized as steps of a mental process that can be performed in the human mind, but for the recitation of generic computer components (such as “at least one processor”, “a communication interface …”, and “a memory device …” (for claims 1 and 15). This can be exemplified by observing data in a user profile and in a user reply and/or training data and making corresponding evaluations to determine a user’s personality and a possible fitting personality with certain traits, to determine an appropriate response to produce by a chat bot, and to determine a sentiment that is associated with the reply, which fall within the “Mental Processes” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the above-indicated limitations are merely instructions to implement the abstract idea on a computer and require no more than a generic computer to perform generic computer functions. Furthermore, the additional elements of “receive a user authentication; retrieve a user profile based on user authentication; record the user personality in the user profile; receive a first indication for a first chat bot response; send the first chat bot response to the user; receive a first user reply in response to the chat bot response; record the first user sentiment in the user profile …; and record the first chat bot response and the first user reply in the user profile....”(in claims 1, 8, and 15) as well as “provide, to the user, a chat bot personality selection; receive, from the user, a user-selected chat bot personality; and record, in the user profile, the user-selected chat bot personality” (in claim 15) all amount to no more than adding insignificant extra-solution activity of mere data gathering, data output, or data recording/saving. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Furthermore, the additional elements of “train an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user personality… ;… the training data is cleaned and transformed prior to training; indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain training data are necessary to improve predictability of the ranking of the user personality; wherein determining the user personality comprises utilizing the trained algorithm configured to determine user personality” (claims 1, 8, and 15) are considered mere usage of a trained machine learning model with generic data preparation, training, iterations, and weighting with no specifics relevant to the practical application, since there is no recitation of the model being trained with specific input/output data in a specific manner that would distinguish it from a generic off-the-shelf component; it merely links at a high level the use of an exception to a certain technological environment and still does not impose any meaningful limits on practicing the abstract idea. In other words, “using” a trained model of a certain kind with a generic feature of “data preparation” and “iteration and weight variations” can be thought of as having a generic off-the shelf component run in the background and not specifically being integrated into actual steps of a practical application since the feedback and weighting are also considered inherent features of many generic machine leaning models, as they are being trained in a generic fashion.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor, a communication interface, and a memory device to perform the determining steps described above amounts to no more than mere instructions to apply the exception using generic computer components. The “receiving/retrieving” steps are further considered well-understood, routine, and conventional in view of the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicating that mere gathering, collection or receipt/transmittal of data over a network is a well-understood, routine, conventional function when claimed in a merely generic manner. Regarding the “providing … a … selection” and “receiving … a user-selected …” steps, as well as the “recording” steps, they are all recited at a high level of generality and are considered similar to exemplary activity which the courts have found to be well-understood, routine, conventional activity when they are claimed in this merely generic manner or insignificant extra-solution activity (See MPEP 2106.05(d) and especially note “Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93”; see also MPEP 2106.05(d) and especially note “Recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016)”). The mere presentation of options and acceptance/recording of user selections as well as recording of the user and system responses, as recited by these limitations of the claims of the instant application are thus also considered well-understood, routine, and conventional. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity nor can the link of the use of the abstract idea to a certain generically recited technological environment. All of these additional elements as generically claimed are considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, these independent claims are not patent eligible.
The dependent claims respectively additionally recite “the user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof” (claims 21, 24, and 27), and “determine the second chat bot response, wherein the second chat bot response comprises second content that is based on the user's profile, and the chat bot personality that is based on the user profile and the first user sentiment based on the first user reply” (claims 23, 26, and 29), which can again be respectively exemplified by observing data in a user profile and in a user reply and/or training data, making corresponding evaluations to determine a user’s personality by contrasting with certain traits to determine a measure or ranking against some standard for each of the traits, and determining an appropriate response to produce by a chat bot based on an evaluation of user’s profile and the chat bot personality that is based on the suer’s profile and the sentiment based on an earlier reply, which analogously fall within the “Mental Processes” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
The dependent claims also additionally recite additional elements of providing, to the user, a chat bot personality selection; receiving, from the user, a user-selected chat bot personality; and recording, in the user profile, the user-selected chat bot personality” (claims 2 and 9), … the chat bot personality selection comprises pre-determined chat bot personalities (claims 3, 10, and 16), determining the first user sentiment comprises natural language processing (claims 4, 11, and 17), and “receiving a second indication for a second chat bot response” (claims 23, 26, and 29).
All of these above-listed additional elements do not serve to integrate the judicial exception into a practical application because they all amount to no more than adding insignificant extra-solution activity related to the steps of providing, receiving, and/or recording (claims 2, 9, 23, 26, and 29) which entail mere presentation of options and acceptance/recording of user selections, the use of natural language processing (NLP) which merely links the use of an exception to a technological environment (claims 4, 11, and 17), or specifications to what the user profile, the selected items, or the chat bot personality comprise/is (claims 3, 10, 16, 22, 25, and 28). They all do not impose any meaningful limits on practicing the abstract idea.
The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the “providing/receiving/recording” steps in claims 2, 9, 23, 26, and 29 are further considered well-understood, routine, and conventional in view of the “Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93” and “Recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016)”court decisions cited in MPEP 2106.05(d) (as addressed with independent claim 15); mere insignificant extra-solution activity cannot provide an inventive concept. Neither can the link of the use of the abstract idea to a certain technological environment, as in claims 4, 11, and 17. Nor do the additional elements of reciting specifics associated with the user profile or the bot personality selection, as in claims 3, 10, 16, 22, 25, and 28. All of these above-listed additional elements, as generically claimed, are thus considered well-understood, routine, and conventional.
Therefore, these limitations, taken alone or in combination, do not integrate the abstract idea into a practical application or recite significantly more that the abstract idea. Thus, all the dependent claims are also not patent eligible.
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, 8, 11, 23, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., US PGPUB 2022/0179888 Al (hereinafter as Wang) in view of Barkan et al., US PGPUB 2009/0070673 Al (hereinafter as Barkan), Tseretopoulos et al., US Patent No. 10,339,931 B2 (hereinafter as Tseretopoulos), and Nassar et al., US PGPUB 2019/0304036 A1 (hereinafter as Nassar).
Regarding independent claim 1, Wang teaches a system for personalizing a chat bot [see e.g. the system in fig. 18; see the personalized chatbot in [0107]] for improved user experience [see e.g. the last 5 lines of [0074]; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight], the system comprising:
at least one processor [see processor 1810 in fig. 18 and [0309]];
a communication interface communicatively coupled to the at least one processor [note e.g. the output interface 1830 in fig 18 and [0309]]; and
a memory device storing executable code [see the memory device options described in [0309]-[0310]] that, when executed, causes the at least one processor [see e.g. [0056]-[0057]] to:
train an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user personality [note training an AI model in [0082]; note the determination of a user personality using a ML model in fig. 13 and indicated in [0187]-[0188] and [0192] as well as [0289]], the training comprising:
iteratively predicting the user personality, based on the set of training data, the set of training data selected from data within users profiles [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], the users profiles comprising users sentiment logs [note the emotion database in fig. 13], users preferences [note the profile database and note personal preferences in [0112]], and users chat histories [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
testing and comparing the user personality predicted during each iteration against a target variable [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality; note in [0153] comparing the user personality to a distribution on each personality category]; and
indicating, via a feedback loop, for each iteration [note the machine-learning feedback loop in fig. 13] whether modifications to weights assigned to certain training data are necessary to improve predictability [note the weight modification in [0083]] of the ranking of the user personality [note in [0187] the personality type ranking described in association with a trained ML model];
retrieve a user profile [note in fig. 2B the user database including different items of information related to a certain user and stored in a database; note the example of extracting the user profile data as in fig. 22A; further note e.g. in [0044] and also on the first 4 lines of [0146] the step of obtaining attribute information based on a certain user input; Examiner notes that the user profile in the instant application refers to all the data related to a certain user as can be seen in fig. 8 and the corresponding description in Applicant’s specifications, such as [0127]-[0138]; on the other hand, Wang uses the term “user database” to refer to the same entity that encompasses all the data related to a certain user whereas the “user profile” is the subset of that data that includes at least one of his gender, age, birth location, occupation, education, etc., in addition to other optional features, as described in [0113]-[0112]; in Wang, the user personality and emotions are referred to directly but are still subsets of the “user database” in Wang’s terminology, i.e. the “user database” in Wang is the entity corresponding to the “user profile” in the instant application];
determine a user personality based on data within the user profile [note in [0146] determining specific user attribute information which include a user’s personality], wherein determining the user personality comprises utilizing the trained algorithm configured to determine user personality [again, note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], and wherein the user profile comprises: a user sentiment log[note the emotion database in fig. 13], user preferences [note the profile database and note personal preferences in [0112]], and user chat history [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
record the user personality in the user profile [note the personality saved in the user database in fig. 13];
receive a first indication for a first chat bot response [note e.g. in the examples in figs. 1A and 1E the receipt of a chat entry from a user (current input of a user) at the system which triggers a chat bot response as per the flow chart in fig. 2C];
determine the first chat bot response, wherein the first chat bot response comprises first content that is based on the user profile and a chat bot personality that is based on the user profile [again, see fig. 2C and note steps S101-S103 which indicate generating a target response based on obtained attribute information; see also the example on the right of fig. 1A using fig.19A indicating determining the response in fig. 1A based on having a childish (versus mature) disposition; see also the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response];
send the first chat bot response to the user [again, see fig. 2C and note step S103 which indicates outputting a target response];
receive a first user reply in response to the first chat bot response [see e.g. the reply from the use in the bottom portion of fig. 1A];
determine a first user sentiment based on the first user reply [note from [0110]-[0111] and from figs.1B determining emotions of the user during the dialogue based on each reply; Examiner notes that in a typical chatbot communication, there is usually multiple entries from the user to the bot];
record the first user sentiment in the user sentiment log of the user profile [again note in fig. 2B the emotion database portion of the user database] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight]; and
record the first chat bot response and the first user reply in the user profile [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight].
Wang does not explicitly teach receiving a user authentication. Neither does it explicitly teach retrieving a user profile based on user authentication. Wang also does not explicitly teach that the first indication for a first chat bot response is the user authentication.
Wang further does not explicitly teach that the users profiles comprise users social media data, users locations, and users transactional data.
Neither does it teach that the training data is cleaned and transformed prior to training.
Barkan teaches a receiving a user authentication and retrieving a user profile based on user authentication [see e.g. [0027]-[0028] indicating an authorization procedure for a certain user and a following retrieval of a user profile associated with that user; note the chat application in [0035]]. Examiner further notes that according to the teachings of Barkan in [0027]-[0028], the user authentication is necessary prior to any further communication with the user which will apply to any applications involving authentication such as the chat indicated in [0035] of Barkan.
It would have been obvious to one of ordinary skill in the art having the teachings of Wang and Barkan before the effective filing date of the claimed invention to modify the instructions/steps taught by Wang by explicitly specifying receiving a user authentication and retrieving a user profile based on user authentication and to further specify that the first indication for a first chat bot response is the user authentication, as per the teachings of Barkan . The motivation for this obvious combination of teachings would be to ensure verifying users that provide correct credentials prior to granting them access to profile data in the system , as suggested by Barkan [again see the scenario presented in [0027]-[0028]].
Wang/Barkan, still, does not explicitly teach that the users profiles comprise users social media data, users locations, and users transactional data.
Neither does it teach that the training data is cleaned and transformed prior to training.
Tseretopoulos teaches user profiles that comprise users social media data, users locations, and users transactional data [see e.g. col. 3, lines 65-66 indicating social network accounts being part of a user profile; see e.g. col. 13, lines 19-21 indicating transaction histories and lines 25-27 indicating user locations].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Tseretopoulos before the effective filing date of the claimed invention to modify the user profiles taught by Wang by explicitly specifying that they comprise users social media data, users locations, and users transactional data, as per the teachings of Tseretopoulos. The motivation for this obvious combination of teachings would be to further personalize responsive outputs automatically generated by a conversation interface by enabling the use of such information to interpret intent of a user input, as suggested by Tseretopoulos [see e.g. col. 5, lines 29-35 as well as col. 13, lines 19-30].
The previously combined art still does not explicitly teach the training data is cleaned and transformed prior to training.
Nassar teaches training data that is cleaned and transformed prior to training [see e.g. [0040] indicating data cleanup and transformation in preparation for training].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Nassar before the effective filing date of the claimed invention to explicitly specify that the training data taught by Wang is cleaned and transformed prior to training, as per the teachings of Nassar. The motivation for this obvious combination of teachings would be to allow a variety of techniques to prepare the training data as needed, as suggested by Nassar [again, see [0040]].
Regarding independent claim 8, Wang also teaches a method [see e.g. title] for personalizing a chat bot [see the personalized chatbot in [0107]] for improved user experience [see e.g. the last 5 lines of [0074]; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight], the method comprising:
training an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user personality [note training an AI model in [0082]; note the determination of a user personality using a ML model in fig. 13 and indicated in [0187]-[0188] and [0192] as well as [0289]], the training comprising:
iteratively predicting the user personality, based on the set of training data, the set of training data selected from data within users profiles [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], the users profiles comprising users sentiment logs [note the emotion database in fig. 13], users preferences [note the profile database and note personal preferences in [0112]], and users chat histories [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
testing and comparing the user personality predicted during each iteration against a target variable [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality; note in [0153] comparing the user personality to a distribution on each personality category]; and
indicating, via a feedback loop, for each iteration [note the machine-learning feedback loop in fig. 13] whether modifications to weights assigned to certain training data are necessary to improve predictability [note the weight modification in [0083]] of the ranking of the user personality [note in [0187] the personality type ranking described in association with a trained ML model];
retrieving a user profile [note in fig. 2B the user database including different items of information related to a certain user and stored in a database; note the example of extracting the user profile data as in fig. 22A; further note e.g. in [0044] and also on the first 4 lines of [0146] the step of obtaining attribute information based on a certain user input; Examiner notes that the user profile in the instant application refers to all the data related to a certain user as can be seen in fig. 8 and the corresponding description in Applicant’s specifications, such as [0127]-[0138]; on the other hand, Wang uses the term “user database” to refer to the same entity that encompasses all the data related to a certain user whereas the “user profile” is the subset of that data that includes at least one of his gender, age, birth location, occupation, education, etc., in addition to other optional features, as described in [0113]-[0112]; in Wang, the user personality and emotions are referred to directly but are still subsets of the “user database” in Wang’s terminology, i.e. the “user database” in Wang is the entity corresponding to the “user profile” in the instant application];
determining a user personality based on data within the user profile [note in [0146] determining specific user attribute information which include a user’s personality], wherein determining the user personality comprises utilizing the trained algorithm configured to determine user personality [again, note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], and wherein the user profile comprises: a user sentiment log[note the emotion database in fig. 13], user preferences [note the profile database and note personal preferences in [0112]], and user chat history [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
recording the user personality in the user profile [note the personality saved in the user database in fig. 13];
receiving a first indication for a first chat bot response [note e.g. in the examples in figs. 1A and 1E the receipt of a chat entry from a user (current input of a user) at the system which triggers a chat bot response as per the flow chart in fig. 2C];
determining the chat bot response, wherein the first chat bot response comprises first content that is based on the user profile and a chat bot personality that is based on the user profile [again, see fig. 2C and note steps S101-S103 which indicate generating a target response based on obtained attribute information; see also the example on the right of fig. 1A using fig.19A indicating determining the response in fig. 1A based on having a childish (versus mature) disposition; see also the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response];
sending the first chat bot response to the user [again, see fig. 2C and note step S103 which indicates outputting a target response];
receiving a first user reply in response to the first chat bot response [see e.g. the reply from the use in the bottom portion of fig. 1A];
determining a first user sentiment based on the first user reply [note from [0110]-[0111] and from figs.1B determining emotions of the user during the dialogue based on each reply; Examiner notes that in a typical chatbot communication, there is usually multiple entries from the user to the bot];
recording the first user sentiment in the user sentiment log of the user profile [again note in fig. 2B the emotion database portion of the user database] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight]; and
recording the first chat bot response and the first user reply in the user profile [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight].
Wang does not explicitly teach receiving a user authentication. Neither does it explicitly teach retrieving a user profile based on user authentication. Wang also does not explicitly teach that the first indication for a first chat bot response is the user authentication.
Wang further does not explicitly teach that the users profiles comprise users social media data, users locations, and users transactional data.
Neither does it teach that the training data is cleaned and transformed prior to training.
Barkan teaches a receiving a user authentication and retrieving a user profile based on user authentication [see e.g. [0027]-[0028] indicating an authorization procedure for a certain user and a following retrieval of a user profile associated with that user; note the chat application in [0035]]. Examiner further notes that according to the teachings of Barkan in [0027]-[0028], the user authentication is necessary prior to any further communication with the user which will apply to any applications involving authentication such as the chat indicated in [0035] of Barkan.
It would have been obvious to one of ordinary skill in the art having the teachings of Wang and Barkan before the effective filing date of the claimed invention to modify the instructions/steps taught by Wang by explicitly specifying receiving a user authentication and retrieving a user profile based on user authentication and to further specify that the first indication for a first chat bot response is the user authentication, as per the teachings of Barkan . The motivation for this obvious combination of teachings would be to ensure verifying users that provide correct credentials prior to granting them access to profile data in the system , as suggested by Barkan [again see the scenario presented in [0027]-[0028]].
Wang/Barkan, still, does not explicitly teach the users profiles comprise users social media data, users locations, and users transactional data.
Neither does it teach that the training data is cleaned and transformed prior to training.
Tseretopoulos teaches user profiles that comprise users social media data, users locations, and users transactional data [see e.g. col. 3, lines 65-66 indicating social network accounts being part of a user profile; see e.g. col. 13, lines 19-21 indicating transaction histories and lines 25-27 indicating user locations].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Tseretopoulos before the effective filing date of the claimed invention to modify the user profiles taught by Wang by explicitly specifying that they comprise users social media data, users locations, and users transactional data, as per the teachings of Tseretopoulos. The motivation for this obvious combination of teachings would be to further personalize responsive outputs automatically generated by a conversation interface by enabling the use of such information to interpret intent of a user input , as suggested by Tseretopoulos [see e.g. col. 5, lines 29-35 as well as col. 13, lines 19-30].
The previously combined art still does not explicitly teach the training data is cleaned and transformed prior to training.
Nassar teaches training data that is cleaned and transformed prior to training [see e.g. [0040] indicating data cleanup and transformation in preparation for training].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Nassar before the effective filing date of the claimed invention to explicitly specify that the training data taught by Wang is cleaned and transformed prior to training, as per the teachings of Nassar. The motivation for this obvious combination of teachings would be to allow a variety of techniques to prepare the training data as needed, as suggested by Nassar [again, see [0040]].
Regarding claims 4 and 11, the rejection of independent claims 1 and 8 are respectively incorporated. Tseretopoulos further teaches that determining user sentiment comprises natural language processing [see col. 17, lines 59-64 indicating the use of NLP to analyze intent (which includes sentiment); see also col. 5, lines 61-67 indicating the use of NLP associated with an emotional state of received input; see also col. 18, lines 19-23].
It would have been obvious to one of ordinary skill in the art having the teachings of the combined art before the effective filing date of the claimed invention to modify the instructions/steps taught by Wang by explicitly specifying that determining the first user sentiment comprises natural language processing, as per the teachings of Tseretopoulos. The motivation for this obvious combination of teachings would be to facilitate real-time emotion determination for a chat discourse utilizing existing available linguistic analysis tools, as suggested by the Tseretopoulos [again see col. 17, lines 59-64] which would enrich the system/method taught by Wang for personalizing chat bot experiences for users by making them faster and more efficient.
Regarding claims 23 and 26, the rejection of independent claims 1 and 8 are respectively incorporated. Wang further teaches:
receiving a second indication for a second chat bot response [note fig. 4, S401 indicating a current input of a user as described in [0241]-[0242] and note from [0240] that the exemplary conversations may be implemented in combination, i.e. a second part of a dialogue (with indication and corresponding response) following an initial one]; and
determining the second chat bot response, wherein the second chat bot response comprises second content that is based on the user's profile, and the chat bot personality that is based on the user profile and the first user sentiment based on the first user reply [note fig. 4, S402 indicating generating a response based on the current input of the user as well as the historical interaction information, as described in [0243]; note e.g. from [0236] that the historical interaction information includes entities in the historical dialogue information and relationship information between the entities; see also [0064] of Wang; further note that the user profile and the first user sentiment are saved (note the profile and the emotion database portions of the user database in fig. 2B) and that the chat bot personality is based on the user profile and sentiment, as per see the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response].
Claims 2, 3, 9, 10, 15-17, 28, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Barkan, Tseretopoulos, Nassar, Seacat DeLuca et al., US PGPUB 2010/0185946 Al (hereinafter as DeLuca), and Douek, US PGPUB 2020/0302263 A1 (hereinafter as Douek).
Regarding claims 2 and 9, the rejection of independent claims 1 and 8 are respectively incorporated. The previously combined art does not explicitly teach:
providing, to the user, a chat bot personality selection;
receiving, from the user, a selected chat bot personality; and
recording, in the user profile, the selected chat bot personality.
Deluca teaches providing, to the user, a chat bot personality selection and receiving, from the user, a selected chat bot personality [see e.g. [0026] indicating allowing a user to select a type of personality for the bot from a list of options and receiving the selection; see also fig. 3].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and DeLuca before the effective filing date of the claimed invention to modify the method taught by the combination by explicitly specifying providing, to the user, a chat bot personality selection and receiving, from the user, a selected chat bot personality, as per the teachings of DeLuca. The motivation for this obvious combination of teachings would be to add an additional personification feature, that is customizable by user elections, to the capabilities of a bot communication, as suggested by DeLuca [see e.g. [0003]; again, see [0026]].
The previously combined art, still, does not explicitly teach recording, in the user profile, the selected chat bot personality.
Douek teaches recording, in a user profile, a selected chat bot personality [see [0029] and note a personalized bot 120 in a dynamic user profile for user 114].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Douek before the effective filing date of the claimed invention to modify the method taught by the combination by explicitly specifying, as per the teachings of Douek, recording, in the user profile, the chat bot personality selected by DeLuca. The motivation for this obvious combination of teachings would be maintain a dynamic user-evolving profile that continuously adapts its user experience from previous interactions, to the capabilities of a bot communication thus providing a consistent uninterrupted experience, as suggested by Douek [see e.g. [0004]-[0005]; [0024]-[0025]; again, see [0029]].
Regarding claims 3 and 10, the rejection of claims 2 and 9 are respectively incorporated. DeLuca further teaches that the chat bot personality selection comprises pre-determined chat bot personalities [note in [0026] that the personalities 172 that the user gets to choose from are stored which indicates that they are pre-determined]. Refer to the rejections of claims 2 and 9 for motivations to combine the cited art.
Regarding independent claim 15, Wang also teaches a system for personalizing a chat bot [see the personalized chatbot in [0107]] for improved user experience [see e.g. the last 5 lines of [0074]; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight], the system comprising:
at least one processor [see processor 1810 in fig. 18 and [0309]];
a communication interface communicatively coupled to the at least one processor [note e.g. the output interface 1830 in fig 18 and [0309]]; and
a memory device storing executable code [see the memory device options described in [0309]-[0310]] that, when executed, causes the at least one processor [see e.g. [0056]-[0057]] to:
train an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user personality [note training an AI model in [0082]; note the determination of a user personality using a ML model in fig. 13 and indicated in [0187]-[0188] and [0192] as well as [0289]], the training comprising:
iteratively predicting the user personality, based on the set of training data, the set of training data selected from data within users profiles [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], the users profiles comprising users sentiment logs [note the emotion database in fig. 13], users preferences [note the profile database and note personal preferences in [0112]], and users chat histories [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
testing and comparing the user personality predicted during each iteration against a target variable [note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality; note in [0153] comparing the user personality to a distribution on each personality category]; and
indicating, via a feedback loop, for each iteration [note the machine-learning feedback loop in fig. 13] whether modifications to weights assigned to certain training data are necessary to improve predictability [note the weight modification in [0083]] of the ranking of the user personality [note in [0187] the personality type ranking described in association with a trained ML model];
retrieve a user profile [note in fig. 2B the user database including different items of information related to a certain user and stored in a database; note the example of extracting the user profile data as in fig. 22A; further note e.g. in [0044] and also on the first 4 lines of [0146] the step of obtaining attribute information based on a certain user input; Examiner notes that the user profile in the instant application refers to all the data related to a certain user as can be seen in fig. 8 and the corresponding description in Applicant’s specifications, such as [0127]-[0138]; on the other hand, Wang uses the term “user database” to refer to the same entity that encompasses all the data related to a certain user whereas the “user profile” is the subset of that data that includes at least one of his gender, age, birth location, occupation, education, etc., in addition to other optional features, as described in [0113]-[0112]; in Wang, the user personality and emotions are referred to directly but are still subsets of the “user database” in Wang’s terminology, i.e. the “user database” in Wang is the entity corresponding to the “user profile” in the instant application];
determine a user personality based on data within the user profile [note in [0146] determining specific user attribute information which include a user’s personality], wherein determining the user personality comprises utilizing the trained algorithm configured to determine user personality [again, note the machine-learning feedback loop in fig. 13 and note updating the user database including the user personality], and wherein the user profile comprises: a user sentiment log[note the emotion database in fig. 13], user preferences [note the profile database and note personal preferences in [0112]], and user chat history [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot];
record the user personality in the user profile [note the personality saved in the user database in fig. 13];
receive a first indication for a first chat bot response [note e.g. in the examples in figs. 1A and 1E the receipt of a chat entry from a user (current input of a user) at the system which triggers a chat bot response as per the flow chart in fig. 2C];
determine the first chat bot response, wherein the first chat bot response comprises first content that is based on the user profile and a chat bot personality that is based on the user profile [again, see fig. 2C and note steps S101-S103 which indicate generating a target response based on obtained attribute information; see also the example on the right of fig. 1A using fig.19A indicating determining the response in fig. 1A based on having a childish (versus mature) disposition; see also the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response];
send the first chat bot response to the user [again, see fig. 2C and note step S103 which indicates outputting a target response];
receive a first user reply in response to the first chat bot response [see e.g. the reply from the use in the bottom portion of fig. 1A];
determine a first user sentiment based on the first user reply [note from [0110]-[0111] and from figs.1B determining emotions of the user during the dialogue based on each reply; Examiner notes that in a typical chatbot communication, there is usually multiple entries from the user to the bot];
record the first user sentiment in the user sentiment log of the user profile [again note in fig. 2B the emotion database portion of the user database] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight]; and
record the first chat bot response and the first user reply in the user profile [note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this phrase is an intended result and thus does not hold any patentable weight].
Wang does not explicitly teach receiving a user authentication. Neither does it explicitly teach retrieving a user profile based on user authentication. Wang also does not explicitly teach that the first indication for a first chat bot response is the user authentication.
Wang further does not explicitly teach that the users profiles comprise users social media data, users locations, and users transactional data. Neither does it teach that the training data is cleaned and transformed prior to training.
Wang also does not teach any of the following:
providing, to the user, a chat bot personality selection;
receiving, from the user, a selected chat bot personality; and
recording, in the user profile, the selected chat bot personality.
Barkan teaches a receiving a user authentication and retrieving a user profile based on user authentication [see e.g. [0027]-[0028] indicating an authorization procedure for a certain user and a following retrieval of a user profile associated with that user; note the chat application in [0035]]. Examiner further notes that according to the teachings of Barkan in [0027]-[0028], the user authentication is necessary prior to any further communication with the user which will apply to any applications involving authentication such as the chat indicated in [0035] of Barkan.
It would have been obvious to one of ordinary skill in the art having the teachings of Wang and Barkan before the effective filing date of the claimed invention to modify the instructions/steps taught by Wang by explicitly specifying receiving a user authentication and retrieving a user profile based on user authentication and to further specify that the first indication for a first chat bot response is the user authentication, as per the teachings of Barkan. The motivation for this obvious combination of teachings would be to ensure verifying users that provide correct credentials prior to granting them access to profile data in the system , as suggested by Barkan [again see the scenario presented in [0027]-[0028]].
Wang/Barkan, still, does not explicitly teach the users profiles comprise users social media data, users locations, and users transactional data. Neither does it teach that the training data is cleaned and transformed prior to training.
It also does not teach any of the following:
providing, to the user, a chat bot personality selection;
receiving, from the user, a selected chat bot personality; and
recording, in the user profile, the selected chat bot personality.
Tseretopoulos teaches user profiles that comprise users social media data, users locations, and users transactional data [see e.g. col. 3, lines 65-66 indicating social network accounts being part of a user profile; see e.g. col. 13, lines 19-21 indicating transaction histories and lines 25-27 indicating user locations].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Tseretopoulos before the effective filing date of the claimed invention to modify the user profiles taught by Wang by explicitly specifying that they comprise users social media data, users locations, and users transactional data, as per the teachings of Tseretopoulos. The motivation for this obvious combination of teachings would be to further personalize responsive outputs automatically generated by a conversation interface by enabling the use of such information to interpret intent of a user input , as suggested by Tseretopoulos [see e.g. col. 5, lines 29-35 as well as col. 13, lines 19-30].
Nassar teaches training data that is cleaned and transformed prior to training [see e.g. [0040] indicating data cleanup and transformation in preparation for training].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Nassar before the effective filing date of the claimed invention to explicitly specify that the training data taught by Wang is cleaned and transformed prior to training, as per the teachings of Nassar. The motivation for this obvious combination of teachings would be to allow a variety of techniques to prepare the training data as needed, as suggested by Nassar [again, see [0040]].
The previously combined art, still, does not explicitly teach:
providing, to the user, a chat bot personality selection;
receiving, from the user, a selected chat bot personality; and
recording, in the user profile, the selected chat bot personality.
Deluca teaches providing, to the user, a chat bot personality selection and receiving, from the user, a selected chat bot personality [see e.g. [0026] indicating allowing a user to select a type of personality for the bot from a list of options and receiving the selection; see also fig. 3].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and DeLuca before the effective filing date of the claimed invention to modify the method taught by the combination by explicitly specifying providing, to the user, a chat bot personality selection and receiving, from the user, a selected chat bot personality, as per the teachings of DeLuca. The motivation for this obvious combination of teachings would be to add an additional personification feature, that is customizable by user elections, to the capabilities of a bot communication, as suggested by DeLuca [see e.g. [0003]; again, see [0026]].
The previously combined art, still, does not explicitly teach recording, in the user profile, the selected chat bot personality.
Douek teaches recording, in a user profile, a selected chat bot personality [see [0029] and note a personalized bot 120 in a dynamic user profile for user 114].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Douek before the effective filing date of the claimed invention to modify the method taught by the combination by explicitly specifying, as per the teachings of Douek, recording, in the user profile, the chat bot personality selected by DeLuca. The motivation for this obvious combination of teachings would be maintain a dynamic user-evolving profile that continuously adapts its user experience from previous interactions, to the capabilities of a bot communication thus providing a consistent uninterrupted experience, as suggested by Douek [see e.g. [0004]-[0005]; [0024]-[0025]; again, see [0029]].
Regarding claim 16, the rejection of claim 15 is incorporated. DeLuca further teaches that the chat bot personality selection comprises pre-determined chat bot personalities [note in [0026] that the personalities 172 that the user gets to choose from are stored which indicates that they are pre-determined]. Refer to the rejections of claim 15 for motivations to combine the cited art.
Regarding claim 17, the rejection of independent claim 15 is incorporated. Tseretopoulos further teaches that determining user sentiment comprises natural language processing [see col. 17, lines 59-64 indicating the use of NLP to analyze intent (which includes sentiment); see also col. 5, lines 61-67 indicating the use of NLP associated with an emotional state of received input; see also col. 18, lines 19-23].
It would have been obvious to one of ordinary skill in the art having the teachings of the combined art before the effective filing date of the claimed invention to modify the instructions/steps taught by Wang by explicitly specifying that determining the first user sentiment comprises natural language processing, as per the teachings of Tseretopoulos. The motivation for this obvious combination of teachings would be to facilitate real-time emotion determination for a chat discourse utilizing existing available linguistic analysis tools, as suggested by the Tseretopoulos [again see col. 17, lines 59-64] which would enrich the system taught by Wang for personalizing chat bot experiences for users by making them faster and more efficient.
Regarding claim 28, the rejection of claim 15 is incorporated. The previously combined art further teaches that the chat bot personality that is based on the user profile is complimentary to the user's personality or that the chat bot personality that is based on the user profile is the user-selected chat bot personality [note again in Deluca, [0026] that the chat bot personality is user-selected]. Refer to the rejection of claim 15 for motivations to combine the cited art.
Regarding claim 29, the rejection of claim 15 is incorporated. Wang further teaches:
receiving a second indication for a second chat bot response [note fig. 4, S401 indicating a current input of a user as described in [0241]-[0242] and note from [0240] that the exemplary conversations may be implemented in combination, i.e. a second part of a dialogue (with indication and corresponding response) following an initial one]; and
determining the second chat bot response, wherein the second chat bot response comprises second content that is based on the user's profile, and the chat bot personality that is based on the user profile and the first user sentiment based on the first user reply [note fig. 4, S402 indicating generating a response based on the current input of the user as well as the historical interaction information, as described in [0243]; note e.g. from [0236] that the historical interaction information includes entities in the historical dialogue information and relationship information between the entities; see also [0064] of Wang; further note that the user profile and the first user sentiment are saved (note the profile and the emotion database portions of the user database in fig. 2B) and that the chat bot personality is based on the user profile and sentiment, as per see the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response].
Claims 21 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Barkan, Tseretopoulos, and Nassar, as applied to claims 1 and 8, above, respectively, and further in view of “Wald, Randall, Taghi Khoshgoftaar, and Chris Sumner. "Machine prediction of personality from Facebook profiles." 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI). IEEE, 2012” (hereinafter as IEEE 2012).
Regarding claims 21 and 24, the rejection of independent claims 1 and 8 are respectively incorporated. The previously combined art does not explicitly teach that the user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof.
IEEE 2012 teaches that a user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof [see e.g. the first paragraph in the IV. Case Study Section on the 3rd page ; see also Table 1].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and IEEE 2012 before the effective filing date of the claimed invention to modify the method/system taught by the combination by explicitly specifying that the user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof, as per the teachings of IEEE 2012. The motivation for this obvious combination of teachings would be to enable classifying human personalities based on a standard trait representation and breakdown of a standard scale, thus relaying specific corresponding personal characteristics, as suggested by IEEE 2012 [again, see the first paragraph in the IV. Case Study Section on the 3rd page].
Claims 22 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Barkan, Tseretopoulos, and Nassar, as applied to claims 1 and 8, above, respectively, and further in view of Lim et al., US Patent No. 10,446,142 B2 (hereinafter as Lim).
Regarding claims 22 and 25, the rejection of independent claims 1 and 8 are respectively incorporated. The previously combined art does not explicitly teach that the chat bot personality that is based on the user profile is complimentary to the user's personality.
Lim teaches a chat bot personality based on the user profile that is complimentary to the user's personality [see e.g. col. 10, lines 51-54].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and Lim, before the effective filing date of the claimed invention, to modify the system taught by the combination by explicitly specifying that the chat bot personality that is based on the user profile is complimentary to the user's personality, as per the teachings of Lim. The motivation for this obvious combination of teachings would be to enable an efficient and balanced communication scheme, as suggested by Lim [see col. 10, lines 31-57].
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Barkan, Tseretopoulos, Nassar, DeLuca, and Douek, as applied to claim 15 above, and further in view of IEEE 2012.
Regarding claim 27, the rejection of independent claim 15 is fully incorporated. The previously combined art does not explicitly teach that the user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof.
IEEE 2012 teaches that a user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof [see e.g. the first paragraph in the IV. Case Study Section on the 3rd page ; see also Table 1].
It would have been obvious to one of ordinary skill in the art having the teachings of the previously combined art and IEEE 2012 before the effective filing date of the claimed invention to modify the system taught by the combination by explicitly specifying that the user personality is determined my measuring a ranking of a user's openness, conscientiousness, extroversion, agreeableness, neuroticism, or a combination thereof, as per the teachings of IEEE 2012. The motivation for this obvious combination of teachings would be to enable classifying human personalities based on a standard trait representation and breakdown of a standard scale, thus relaying specific corresponding personal characteristics, as suggested by IEEE 2012 [again, see the first paragraph in the IV. Case Study Section on the 3rd page].
Response to Arguments
Regarding the pending claim rejections under 35 U.S.C. 101 for the claims being directed to a judicial exception without significantly more, Examiner respectfully reiterates that the amended limitations of the independent claims are analogous to some of the limitations of claim 2 of Example 47 in the “July 2024 Subject Matter Eligibility Examples”. In light of these guidelines, Examiner notes that, contrary to Applicant’s allegations, the iterative prediction, comparison, and determination limitations indeed recite a mental process that is based on observations and evaluations that can be performed in the human mind. Therefore, the claims recite a judicial exception.
Applicant lists on the bottom of p. 8 of the Response recited additional elements of amended claim 1. Examiner respectfully notes that none of these suffice to integrate the abstract idea into a practical application, as each of these either belongs to a mere implementation of a generic computer to perform generic computer functions or confines the use of the abstract idea to a particular technological environment of training and making determination utilizing the trained algorithm and thus fails to add an inventive concept to the claims. Therefore, all of the additional elements still do not integrate the abstract idea into a practical application. On the bottom of p. 9 of the response, Applicant argues that the iterative process necessarily improves the accuracy of the machine learning model. Examiner respectfully reiterates that mere recitation of a generic iterative approach with weight modification in the training of a model can still be thought of as having a generic off-the shelf component run in the background and not specifically being integrated into actual steps of a practical application since the feedback, iteration, and weighting are considered inherent features of many generic machine leaning models, as they are being trained in a generic fashion. The same argument applies to training data preparation. Regarding the limitations reciting “to improve the chatbot personality over time…”, Examiner respectfully notes that this limitation merely indicates an intended result without specifying how this result is to be achieved and thus does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Finally, Examiner also reiterates that none of the additional elements suffices to amount to significantly more than the judicial exception because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Neither can insignificant extra-solution activity nor can the link of the use of the abstract idea to a certain generically recited technological environment. Applicant is referred to the full rejections above for further details.
The rejections under 35 U.S.C. 101 are thus respectfully maintained and are updated in view of the amended limitations, as presented above.
Applicant’s prior art arguments regarding amended independent claims 1 and 8 have been fully considered but are not persuasive.
First, regarding Applicant’s arguments against the teachings of Wang on pp. 15-16, Examiner respectfully notes that Wang clearly teaches the feature of training an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user personality [note training an AI model including weight modification in [0082]-[0083]; note the determination of a user personality using a ML model in fig. 13 (especially note the machine-learning feedback loop in fig. 13 and updating the user database including the user personality); see also [0187]-[0188] and [0192] as well as [0289] and the other portions cited in the rejection, as updated above in view of the amended language including specific recitations of the training limitations and the user personality determination].
Next, regarding the limitation reciting “determining a user personality based on data within the user profile, … wherein the user profile comprises users social media data, users locations, and users transactional data”, Examiner respectfully notes that Wang clearly teaches determining a user personality based on data within the user profile. Tseretopoulos teaches user profiles that comprise users social media data, users locations, and users transactional data [see e.g. col. 3, lines 65-66 indicating social network accounts being part of a user profile; see e.g. col. 13, lines 19-21 indicating transaction histories and lines 25-27 indicating user locations]. It would have been obvious to modify the user profiles taught by Wang by explicitly specifying that they comprise users social media data, users locations, and users transactional data, as per the teachings of Tseretopoulos and to utilize the data in the modified user to determine the user personality, as initially taught by Wang. Therefore, the combined art sufficiently and clearly teaches the argued limitation. Examiner reminds Applicant that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Finally, Applicant is referred to the full rejections, as presented above, including teachings of the newly cited art, Nassar, and motivations to combine regarding the amendment reciting that the training data is cleaned and transformed prior to training.
Thus, Examiner respectfully asserts that the combination of the cited art sufficiently teaches each and every limitations recited in amended independent claim 1. Analogous rationale holds for amended independent claim 8. For a more detailed analysis, please refer to the full rejections above.
Additional prior art arguments regarding amended independent claim 15 have been fully considered but are not persuasive.
Applicant argues that “Deluca determines the chatbot personality and response based solely on the user selected personality. This is not equivalent to claim 15, which bases the chat bot response on both the user selected chatbot personality and the user profile which includes the user sentiment log which records user sentiment and responses to improve the chatbot personality over time.”
Examiner notes that Wang is relied upon to teach determining the first chat bot response, wherein the first chat bot response comprises first content that is based on the user profile and a chat bot personality that is based on the user profile [again, see fig. 2C and note steps S101-S103 which indicate generating a target response based on obtained attribute information; see also the example on the right of fig. 1A using fig.19A indicating determining the response in fig. 1A based on having a childish (versus mature) disposition; see also the examples in fig. 1E and 11A indicating determining the response based on aspects of the user attributes in the profile and personality; see also e.g. fig. 23 indicating using user personality features which are part of a user’s data for determining a chat bot response]. Wang further teaches that the user profile comprises: a user sentiment log [note the emotion database in fig. 13] and recording the first user sentiment and the responses in the user profile [again note in fig. 2B the emotion database portion of the user database; note in [0166] the historical dialogue information which is stored from interactions between the user and the chatbot] to improve the chatbot personality over time by increasing conversability and likability [note in [0166] the improvement in the chatting process because of stored information; see also in [0097] and [0099] the use of profile information to increase likeability and conversability; Examiner also notes that this last phrase is an intended result and thus does not hold any patentable weight]. The only missing aspect in the indicated limitation is that the chatbot personality is user-selected. Deluca is relied upon to teach a user-selected chatbot personality [see e.g. [0026] indicating allowing a user to select a type of personality for the bot from a list of options and receiving the selection; see also fig. 3]. Again, it would have been obvious to add a selection option and to receive a user selection to the framework taught by Wang to add an additional personification feature, that is customizable by user elections, to the capabilities of a bot communication, as suggested by DeLuca [see e.g. [0003]; again, see [0026]]. Therefore, the combined art sufficiently and clearly teaches the argued limitation. Again, Examiner reminds Applicant that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Thus, Examiner respectfully asserts that the combination of the cited art sufficiently teaches each and every limitations recited in amended independent claim 15. For a more detailed analysis, please refer to the full rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Examiner notes the following teachings from the cited art:
US 2018/0336048 A1, Zarlengo et al., which interactive virtual assistants that take the user personality and emotions into consideration [see e.g. [0143]].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIA S AYAD whose telephone number is (571)272-2743. The examiner can normally be reached Monday-Friday, 7:30 am - 4:30 pm. Alt, Friday, EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached at (571) 272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARIA S AYAD/Primary Examiner, Art Unit 2172