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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 13, 2026 has been entered.
Claims 1 and 11 have been amended. Claims 9 and 19 have been cancelled. Claims 1-8, 10-18 and 20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 10-18 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; determining, based on the user workflow, an activity target for the user; presenting, by the web server, a user interface capable of accepting freeform text input from the user; receiving, by the web server, a character string via the user interface; generating a vector encoding of the character string wherein the generating the vector encoding of the character string comprises applying a machine learning model to the character string to generate vector encoding and applying the machine model to reduce a dimensionality parameter of the freeform text; calculating, based on the vector encoding, a user sentiment score for the character string; dynamically generating, based on the user sentiment score, a response to the character string in real-time, wherein the response to the character string contains information routing the user to an updated path to the activity target; transmitting, by the server, the generated response to a user device; and causing the user device to operate the user interface to provide a visual representation of the generated response in real-time. The step for “obtaining…” is a data gathering step that can be achieved by the a person accessing and reviewing user interactions; the step for “determining…” can be achieved by the person reviewing the interactions and using mental processing, identifying a pertinent activity; the step for “presenting…” can be achieved by the person, using pen and paper, a list of questions/survey to the user; the step for “receiving…” is a data gathering step that can be achieved by the person retrieving the paper with the user’s input written on it; the step for “generating…” can be achieved by, using pen and paper, language processing techniques, and natural language model rules and principles, organizing text in vector format in a specific reduced dimensionality format; the step for “calculating..” can be achieved by the user analyzing the vector and determining a sentiment score for the text; the step for “dynamically generating….” can be achieved by the person determining the appropriate response to the user’s input using known natural language processing rules/algorithms fundamentals via mental processing or pen and paper; the step for “transmitting…and causing…” can be achieved by the person, using pen and paper, presenting the response.
Claim 11 recites a system comprising: a memory configured to store (a) one or more vector encodings of textual data and (b) information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session; and at least one processor configured to: determine, based on the user workflow, an activity target for the user; transmit, to a user device, a user interface capable of accepting freeform text input from the user; receive one or more character strings via the user interface; generate a vector encoding of the one or more character strings wherein the generating the vector encoding of the character string comprises applying a machine learning model to the character string to generate vector encoding and applying the machine model to reduce a dimensionality parameter of the freeform text; calculate, based on the vector encoding, a user sentiment score for the character string; dynamically generate, based on the user sentiment score, a response to the character string in real-time, wherein the response to the character string contains information routing the user to an updated path to the activity target; transmit, via the user interface, the generated response to the user device; and cause the user device to operate the user interface to provide a visual representation of the generated response in real-time. The feature to “store…” is a data gathering and organizing step that can be achieved by the a person accessing and reviewing user interactions and formatted textual data; the step for “determining…” can be achieved by the person reviewing the interactions and using mental processing, identifying a pertinent activity; the step to “transmit…” can be achieved by the person, using pen and paper, giving a list of questions/survey to the user; the step to “receive…” is a data gathering step that can be achieved by the person retrieving the paper with the user’s input written on it; the step to “dynamically generate…” can be achieved by, using pen and paper, language processing techniques, and natural language model rules and principles, organizing text in vector format in a specific reduced dimensionality format; the step to “calculate..” can be achieved by the user analyzing the vector and determining a sentiment score for the text; the step to “dynamically generate….” Can be achieved by the person determining the appropriate response to the user’s input; the step to “transmit…and cause” can be achieved by the person, using pen and paper, presenting the response.
The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computer, apparatus, computer program product, and generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computer components, device and various modules amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the computer components, computing device and modules to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-8, 12-18 and 20 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating functions and commands for the input text, applying natural language processing models and techniques to process input text, determine sentiment scores and generating responses, determining and identifying activities for user, determining when and how a human user will participate, and using pen and paper, displaying outputs.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 1-8, 10-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lam (US Patent Application Publication No. 2019/0114321) in view of Qadrud-Din et al (US Patent No. 11,409,752), hereinafter Qadrud-Din.
Lam teaches an auto tele-interview solutions with improved generation and control of conversations. Regarding claim 1, Lam teaches a method for directing textual conversation based on user workflow, the method comprising: obtaining, by a web server, information regarding a user workflow, the information regarding the user workflow representing a progression of one or more user interfaces with which a user has interacted within a session [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; determining, based on the user workflow, an activity target for the user [next question -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; presenting, by the web server, a user interface capable of accepting freeform text input from the user [0019; 0075]; receiving, by the web server, a character string via the user interface [user responses -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; calculating a user sentiment score for the character string [disposition score – p0008; 0076]; dynamically [para 0019 – dynamic interface objects] generating, based on the user sentiment score, a response to the character string in real-time, wherein the response to the character string contains information routing the user to an updated path to the activity target [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0111 -- display a request to confirm the changes and then ask additional questions as required depending on the changes from the user’s input; 0126-0155; 0163-0164]; transmitting, by the web server the generated response to the user device [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; and causing the user device to operate the user interface to provide a visual representation of the generated response in real-time [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0111 -- display a request to confirm the changes and then ask additional questions as required depending on the changes; 0126-0155; 0163-0164]. Lam fails to teach, but Qadrud-Din teaches generating a vector encoding of a character string wherein the generating the vector encoding of the character string comprises applying a machine learning model to the character string to generate vector encoding and applying the machine model to reduce a dimensionality parameter of the freeform text [col. 2, lines 7-19; col. 2, lines 24-42; col. 12, line 51 to col. 14, line 11]. Therefore, one having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the vector encoding processing suggested by Qadrud-Din, in the conversation system of Lam, and the results would have been predictable and would provide for improved efficiency of system processing and greater flexibility of application in storage and/or resource constrained implementations, as suggested by Qadrud-Din and therefore provide an improved system and interaction to the user.
Regarding claim 2, the combination of Lam and Qadrud-Din teaches obtaining, by the web server, information regarding a continued user workflow [structured workflow of series of conversation decisions – para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164]; determining, from the continued user workflow, whether the user completed the activity target [para 0096; 0119]; modifying the user sentiment score based on whether the user completed the activity target [disposition score – p0008; 0076]; and taking one or more responsive actions based on the modified user sentiment score [next question -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 3, the combination of Lam and Qadrud-Din teaches where the user interface is a chat application permitting real-time exchange of text between a user device and a remote system, wherein the character string is provided by the user as freeform text entry into the chat application and the generated response to the character string is provided to the user in the chat application in reply to the freeform text entry into the chat application [para 0019; 0067].
Regarding claim 4, the combination of Lam and Qadrud-Din teaches of the response to the character string is further based on at least one of user location data and user language data stored, in a memory, in association with information identifying the user [user profile - para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 5, the combination of Lam and Qadrud-Din teaches the calculating of the user sentiment score for the character string comprises: applying one or more natural language processing (NLP) models to perform a sentiment analysis of the character string [disposition score – p0008; 0076].
Regarding claim 6, the combination of Lam and Qadrud-Din teaches updated path to the activity target comprises at least one of: a self-solve workflow, an agent-controlled workflow, and a cancellation workflow [chat with live agent – para 0019; 0067 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 7, the combination of Lam and Qadrud-Din teaches: (a) displaying, via the user interface, one or more instructions regarding a self-solve action, and (b) generating a support ticket and transmitting the support ticket, via a network, to a support agent [chat with live agent – para 0019; 0067; 0075 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 8, the combination of Lam and Qadrud-Din teaches the support agent is a human actor [chat with live agent – para 0019; 0067 -para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Regarding claim 10, the combination of Lam and Qadrud-Din teaches generating a response to the character string comprises: applying a machine learning model to the user sentiment score and available self-solve actions to generate the response to the character string [neural network system -- para 0012-0015; 0018; 0025; 0066-0068; 0069-0086; 0090—0093; 0126-0155; 0163-0164].
Claims 11- 18 and 20 are rejected under similar rationale as claims 1-8 and 10.
Response to Arguments
Applicant's arguments filed January 13, 2026 with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues “Applicant discloses a specific manner in which machine learning model is trained and applied to define a large data set.” The Examiner notes, the claims do not recite features for how the machine learning model is trained or specifically applied to define large data set. Further the specification does not disclose or describe how the model is specifically trained or applied in the realization of the invention. As Applicant’s disclosure fails to specifically describe how the model is trained, operates or learn, a technical improvement is not sufficiently identified or described. Accordingly, the 35 USC 101 rejections are maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659