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 .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-20 are currently pending and have been considered below.
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 - 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-14 are directed to a system (i.e. a machine). Claims 15-20 are directed to a method (i.e. a process). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Representative independent claim 15, analogous to independent claims 1 and 10 recites: A method for interacting between a bank agent and a client of the bank over a channel, said method comprising: asking the client an initial question; providing an answer by the client to the initial question; providing a follow-up question in response to the answer provided to the initial question that is generated; accepting an answer to the follow-up question; providing additional follow-up questions in response to previous questions and answers that are generated, provide the questions based on the previous questions and answers; and providing a bank service or product based on all of the questions and answers.
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). As the claims discuss interacting between a bank agent and a client of the bank, which is a clear business relation and one of certain methods of organizing human activity. Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (independent claim 15 recites for example, “asking the client an initial question”; “providing an answer by the client to the initial question”; “providing a follow-up question in response to the answer provided to the initial question”; “accepting an answer to the follow-up question”; “providing additional follow-up questions in response to previous questions and answers that are generated”, “provide the questions based on the previous questions and answers”; “providing a bank service or product based on all of the questions and answers”). Concepts performed in the human mind as mental processes because the steps of asking a question, providing an answer, providing, accepting and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
Further, dependent claims add additional limitations, for example: (claim 2) wherein the questions are provided by a bank agent to a client of the bank over a channel, the client provides the answers to the questions to the agent and the agent provides the answers to the processor, and the solution is a financial product or service (claim 5 and 18) questions and answers are displayed (claims 6, 13 and 19) stores data and information including name, address, account types, and account balances of the client that is used to provide the questions (claims 7, 14 and 20) stores data and information for each interaction and transaction between all of the banks clients and the bank over all banking channels that is used to provide the questions, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of certain methods of organizing human activity and mental processes, but for the recitation of generic computing components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (independent claims) back-end server; processor; interface; memory device; machine learning model; neural network; nodes that have been trained (claims 3, 11 and 16) telephonic channel (claims 4, 12 and 17) messaging channel; (claim 9) convolutional neural network (CNN) or a recurrent neural network (RNN). These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
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 discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-20 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Izenson et al. (US 2021/0191926 A1), hereinafter “Izenson”, over Thirles et al. (US 2023/0342821 A1), hereinafter “Thirles”.
Regarding Claim 1, Izenson teaches A system for autonomously providing follow-up questions to answers provided to previous questions, said system comprising: (See at least Izenson, Abstract, teaches processing a query from a user... receiving, by a server computer, an initial question from a client computer... the server computer can determine a list of clarifying questions based on a subset of the set of words. The server computer presents the list of clarifying questions to the client computer and receiving clarifying answers to the clarifying questions); a back-end server including: (Izenson, Abstract, a server computer);
at least one processor for processing data and information; (See at least Izenson, para 0006, teaching processor and computer environment);
a communications interface communicatively coupled to the at least one processor; and (See at least Izenson, para 0032, teaches interfaces);
a memory device storing data and executable code that, when executed, causes the at least one processor to: (Memory is taught throughout Izenson, see at least para 0040-0042);
accept an initial question; (Izenson, Abstract, an initial question from a client computer);
accept an answer to the initial question; (Izenson, Abstract, an answer to the initial question);
provide a follow-up question in response to the answer provided to the initial question using a machine learning model; (Machine learning is taught throughout Izenson, see at least para 0015, The question and the set of clarifying questions can be used for a machine learning model to refine the clarifying questions and develop new clarifying questions);
accept an answer to the follow-up question; (Answering questions is throughout Izenson, see at least para 0014-0015);
provide additional follow-up questions in response to previous questions and answers using the machine learning model; and (See at least Izenson, para 0015, machine learning model to refine the clarifying questions and develop new clarifying questions). Yet Izenson does not appear to explicitly teach and in the same field of endeavor Thirles teaches provide a solution based on all of the questions and answers (See at least Thirles, Abstract, introduce new solutions based on data gathering and processing). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Izenson with provide a solution based on all of the questions and answers as taught by Thirles with the motivation for identifying new solutions and transmitting a communication to the user to propose the new solutions (Thirles, Abstract). The Izenson invention now incorporating the Thirles invention, has all the limitations of claim 1.
Regarding Claim 2, Izenson, now incorporating Thirles, teaches The system according to claim 1 wherein the questions are provided by a bank agent to a client of the bank over a channel, the client provides the answers to the questions to the agent and the agent provides the answers to the processor, and the solution is a financial product or service (Izenson teaches questions and answers throughout, see at least Abstract; Izenson, para 0031, teaches communication network. Thirles teaches bank and clients throughout, see at least Thirles, para 0004, teaches providing recommendations to the client for certain bank products and services based on the needs of the client).
Regarding Claim 3, Izenson, now incorporating Thirles, teaches The system according to claim 2 wherein the channel is a telephonic channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email or phone call; Further, Thirles, para 0023-0024, teaches phones.)
Regarding Claim 4, Izenson, now incorporating Thirles, teaches The system according to claim 2 wherein the channel is a computer messaging channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email).
Regarding Claim 5, Izenson, now incorporating Thirles, teaches 5. The system according to claim 2 wherein the questions and answers are displayed on a computer screen visible to the agent (Display is taught throughout Izenson, see at least para 0081, teaches display the question asked by the user; para 0083, display answers to the questions).
Regarding Claim 6, Izenson, now incorporating Thirles, teaches The system according to claim 2 wherein the memory device stores data and information including name, address, account types, and account balances of the client that is used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; See at least Thirles, para 0081, teaches database that stores identifying data and information, such as name, address, account types, account balances, etc.)
Regarding Claim 7, Izenson, now incorporating Thirles, teaches The system according to claim 2 wherein the memory device stores data and information for each interaction and transaction between all of the banks clients and the bank over all banking channels that is used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; Izenson, para 0039, teaches Data warehouse may comprise a plurality of databases. The databases may be integrated to bring together data from a plurality of sources. The data in the data warehouse may include, for example, transaction data, organizational data, user data; Further, Thirles, para 0081, teaches database that stores identifying data and information for all of the clients of the bank.)
Regarding Claim 8, Izenson, now incorporating Thirles, teaches The system according to claim 2 wherein the machine learning model uses at least one neural network having nodes that have been trained to provide the questions based on the previous questions and answers (See at least Izenson, para 0070, teaches machine learning model may be a neural network; Further, Thirles teaches neural network throughout, see at least para 0082, teaches one or more neural networks having trained nodes).
Regarding Claim 9, Izenson, now incorporating Thirles, teaches The system according to claim 8 wherein the at least one neural network is a convolutional neural network (CNN) or a recurrent neural network (RNN) (Thirles teaches neural network throughout, see at least para 0082, machine learning processors and algorithms can employ some or all of the various neural network types such as CNNs and RNNs).
Regarding Claim 10, Izenson teaches A system for autonomously providing follow-up questions to answers provided to previous questions, wherein the questions are provided by a ... over a channel and the client provides the answers to the questions to the agent, and wherein the questions and answers are displayed on a computer screen visible to the agent, said system comprising: (See at least Izenson, Abstract, teaches processing a query from a user... receiving, by a server computer, an initial question from a client computer... the server computer can determine a list of clarifying questions based on a subset of the set of words. The server computer presents the list of clarifying questions to the client computer and receiving clarifying answers to the clarifying questions);
a back-end server including: (Izenson, Abstract, a server computer);
at least one processor for processing data and information; (See at least Izenson, para 0006, teaching processor and computer environment);
a communications interface communicatively coupled to the at least one processor; and (See at least Izenson, para 0032, teaches interfaces);
a memory device storing data and executable code that, when executed, causes the at least one processor to: (Memory is taught throughout Izenson, see at least para 0040-0042);
accept an initial question; (Izenson, Abstract, an initial question from a client computer);
accept an answer to the initial question; (Answering questions is throughout Izenson, see at least Izenson, Abstract and para 0014-0015);
provide a follow-up question in response to the answer provided to the initial question that is generated by a machine learning model; (Machine learning is taught throughout Izenson, see at least para 0015, The question and the set of clarifying questions can be used for a machine learning model to refine the clarifying questions and develop new clarifying questions);
accept an answer to the follow-up question; (Answering questions is throughout Izenson, see at least para 0014-0015);
provide additional follow-up questions in response to previous questions and answers that are generated by the machine learning model, wherein the machine learning model uses at least one neural network having ... that have been trained to provide the questions based on the previous questions and answers; and (See at least Izenson, para 0015, machine learning model to refine the clarifying questions and develop new clarifying questions; para 0070, teaches machine learning model may be a neural network). Yet Izenson does not appear to explicitly teach and in the same field of endeavor Thirles teaches bank agent to a client of the bank ...nodes ... provide a bank service or product based on all of the questions and answers (Banks and financing is taught throughout Thirles, see at least para 0002; Further, See at least Thirles, Abstract, introduce new solutions based on data gathering and processing; para 0082, teaches solutions to the client, such as recommending specific products or services; Nodes and neural networks are taught throughout Thirles, see at least para 0060). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Izenson with bank agent to a client of the bank ... nodes ... provide a bank service or product based on all of the questions and answers as taught by Thirles with the motivation for identifying new solutions and transmitting a communication to the user to propose the new solutions (Thirles, Abstract). The Izenson invention now incorporating the Thirles invention, has all the limitations of claim 10.
Regarding Claim 11, Izenson, now incorporating Thirles, teaches The system according to claim 10 wherein the channel is a telephonic channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email or phone call; Further, Thirles, para 0023-0024, teaches phones.)
Regarding Claim 12, Izenson, now incorporating Thirles, teaches The system according to claim 10 wherein the channel is a computer messaging channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email).
Regarding Claim 13, Izenson, now incorporating Thirles, teaches The system according to claim 10 wherein the memory device stores data and information including name, address, account types, and account balances of the client that is used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; See at least Thirles, para 0081, teaches database that stores identifying data and information, such as name, address, account types, account balances, etc.)
Regarding Claim 14, Izenson, now incorporating Thirles, teaches The system according to claim 10 wherein the memory device stores data and information for each interaction and transaction between all of the banks clients and the bank over all banking channels that is used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; Izenson, para 0039, teaches Data warehouse may comprise a plurality of databases. The databases may be integrated to bring together data from a plurality of sources. The data in the data warehouse may include, for example, transaction data, organizational data, user data; Further, Thirles, para 0081, teaches database that stores identifying data and information for all of the clients of the bank.)
Regarding Claim 15, Izenson teaches A method for interacting between ... over a channel, said method comprising: (See at least Izenson, Abstract, teaches processing a query from a user... receiving an initial question from a client computer... receiving clarifying answers to the clarifying questions; Further, Izenson, para 0031, teaches communication network);
asking the client an initial question; (Izenson, Abstract, an initial question from a client computer);
providing an answer by the client to the initial question; (Izenson, Abstract, an answer to the initial question);
providing a follow-up question in response to the answer provided to the initial question that is generated by a machine learning model in a processor; (Machine learning is taught throughout Izenson, see at least para 0015, The question and the set of clarifying questions can be used for a machine learning model to refine the clarifying questions and develop new clarifying questions);
accepting an answer to the follow-up question; (Answering questions is throughout Izenson, see at least para 0014-0015);
providing additional follow-up questions in response to previous questions and answers that are generated by the machine learning model, wherein the machine learning model uses at least one neural network having ... that have been trained to provide the questions based on the previous questions and answers; (See at least Izenson, para 0015, machine learning model to refine the clarifying questions and develop new clarifying questions; para 0070, teaches machine learning model may be a neural network). Yet Izenson does not appear to explicitly teach and in the same field of endeavor Thirles teaches a bank agent and a client of the bank ...nodes ... providing a bank service or product based on all of the questions and answers (Banks and financing is taught throughout Thirles, see at least para 0002; Further, See at least Thirles, Abstract, introduce new solutions based on data gathering and processing; para 0082, teaches solutions to the client, such as recommending specific products or services; Nodes and neural networks are taught throughout Thirles, see at least para 0060). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Izenson with a bank agent and a client of the bank ... nodes ... providing a bank service or product based on all of the questions and answers as taught by Thirles with the motivation for identifying new solutions and transmitting a communication to the user to propose the new solutions (Thirles, Abstract). The Izenson invention now incorporating the Thirles invention, has all the limitations of claim 15.
Regarding Claim 16, Izenson, now incorporating Thirles, teaches The method according to claim 15 wherein the channel is a telephonic channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email or phone call; Further, Thirles, para 0023-0024, teaches phones.)
Regarding Claim 17, Izenson, now incorporating Thirles, teaches The method according to claim 15 wherein the channel is a computer messaging channel (See at least Izenson, para 0031, teaches communication network; Izenson, para 0090, teaches email).
Regarding Claim 18, Izenson, now incorporating Thirles, teaches The method according to claim 15 wherein the questions and answers are displayed on a computer screen visible to the agent (Display is taught throughout Izenson, see at least para 0081, teaches display the question asked by the user; para 0083, display answers to the questions).
Regarding Claim 19, Izenson, now incorporating Thirles, teaches The method according to claim 15 wherein data and information including name, address, account types, and account balances of the client are used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; See at least Thirles, para 0081, teaches database that stores identifying data and information, such as name, address, account types, account balances, etc.)
Regarding Claim 20, Izenson, now incorporating Thirles, teaches The method according to claim 15 wherein data and information for each interaction and transaction between all of the banks clients and the bank over all banking channels are used by the processor to provide the questions (Izenson teaches questions and answers throughout, see at least Abstract; Izenson, para 0039, teaches Data warehouse may comprise a plurality of databases. The databases may be integrated to bring together data from a plurality of sources. The data in the data warehouse may include, for example, transaction data, organizational data, user data; Further, Thirles, para 0081, teaches database that stores identifying data and information for all of the clients of the bank.)
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Balanai et al. US 2015/0178623 A1 - "In one embodiment, post-processing comprises ordering questions by similarity, merging similar questions with the same answer, scoring or ranking similar questions with different answers"
Dettman US 9/542,496 B2 - "responsive to the set of question characteristics found in the received input question failing to matching the question characteristics associated with one or more previous questions in the set of previous questions above the related-question predetermined"; "matching the question characteristics associated with one or more previous questions in the set of previous questions above a related-question predetermined threshold.”
Kern et al. US 10956822 - "the closely related questions having answers associated with each question of the closely related questions”; “the knowledgebase stores question answers that are associated with each question of the stored questions. From the determination of closely related questions"; "iteratively answering each question of the set of questions by determining the set of related questions and associated answers."
Fuggetta US 2015/0278899 A1 - "System and Method for Providing Customer' Answers to Prospects' Questions In Digital Media"
NPL – Moksha Thisarani; Subha Fernando, “Artificial Intelligence for Futuristic Banking”, Published in: 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC); Date of Conference: 21-23 June 2021, https://ieeexplore.ieee.org/document/9570253 - Artificial Intelligence (AI) has become an essential resource for large banks that deal with regulatory changes, new Anti-Money Laundering (AML) obligations and vulnerable fraud-prone clients. Cybersecurity has thus become a hot topic due to security failures using traditional methods and concerns about how companies use the personal data collected from clients or their regular users. The most obvious apparent reason why cybersecurity is critical in banking sector transactions is to protect client assets with a high level of data privacy. The main approaches in the front office conventional banking such as AI chatbots, smart virtual assistants and biometric user authentication are discovered to answer security challenges and to enhance prosperity in the field. Concurrently, advanced AI applications in fraud detection, fraud risk monitoring, anti-money laundering techniques and cross-border payments handling are observed under the back-office operations. The paper reviews the conceptualizations of privacy concerns and the antecedents and consequences of using AI-power in the banking sector. Moreover, overlooked limitations of AI such as scarcity of quality data, a rise of hidden-bias in suggestions and obliviousness of lacking knowledge are discussed with several thriving solutions.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
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/R.R.N./Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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