Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant’s arguments with respect to claims 1, 3, 9, 11, 15 and 20 have been considered but are unpersuasive.
Applicant argues:
A) Independent Claim 1
Brock does not disclose housing market data that is accessed based at least in part on the current address of the user from the account history. It is acknowledged Brock does not explicitly state using a stored billing address to retrieve housing data. However, Brock teaches storing user addresses in a subscriber account database (p[0040] and retrieving real estate information for user specific geographic market (p[0083]-[0085]). It would have been obvious to a person of ordinary skill in the art to use the stored address to automatically personalize the geographic region for data retrieval, thereby streamlining the user’s interaction with the system. This optimization is a predictable design choice consistent with Brock’s teachings and satisfies the limitation.
Satterfield does not disclose sending a notification in response to changing market conditions. The Examiner respectfully disagrees. While Satterfield’s primary examples concern credit score changes, it discloses systems that generate notifications in response to changes in external financial data, such as interest rates and product eligibility (Col 10:57-65). These teachings would reasonably encompass notifications based on updated housing market data, particularly when such data is already retrieved by the system as taught by Brock. The motivation to adapt Satterfield’s system to notify users of housing market changes is a routing substitution of known triggers within an otherwise analogous use case.
The cited references do not disclose sentiment analysis, specifically sentiment analysis and normalization. The additional limitations were formerly recited in cancelled claims 2, 10 and 16. These claims bring in additional matter not considered in further dependent claims 4-9, 12-14 and 18-20. The argument is moot based on new grounds of rejection.
Claim Rejections - 35 USC § 103
3. 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.
4. The factual inquiries 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 nonobviousness.
5. Claims 1, 3-8, 9, 11-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky (US 11,847,420) in view of Peddada (US 10,412,068), further in view of Ahmadidaneshashtiani (US 2023/0377567) herein ‘567, further in view of Satterfield (US 7,542,993) and further in view of Brock (US 2002/0035535).
Regarding Claim 1:
Galitsky discloses a computer-implemented method, comprising:
(Galitsky: Fig. 1 shows a computing device associated with a user);
receiving, by the computing device, a query from the user device, the query comprising one or more mortgage questions (Galitsky: Col 6 lines 5-19 are examples of a received user query regarding a mortgage additionally Fig. 1 shows question answering engine);
accessing, by the computing device, an account history comprising financial information associated with the user, a query history for the user (Galitsky: Col 5 lines 47-56 discloses account
history such as last time since credit inquiry, credit report or average age of accounts which is used by
a machine learning model),
providing, by the computing device, the query, the account history, and the housing market data, as an input to a virtual assistant application that generates a response to the query (Galitsky: Fig. 10 display that data and the query is given to the machine learning model and it may be about mortgage questions which would necessarily require housing market data to answer);
receiving, by the computing device, the response as an output from the virtual assistant applications (Galitsky: Fig. 11 1105 provide a response to the user query based at least in part on the
explanation chain and the set of decision features);
Galitsky does not disclose authenticating, by an application programming interface (API) of a computing device, an account of a user that is associated with a user device, however, Peddada discloses this limitation (Peddada: Col 10-28 discloses an API used to generate and authenticate a connection with a user on a computing device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Galitsky’s system to include API-based authentication as taught by Peddada to improve the security of user account access. Galitsky already discloses a method for receiving queries relating to mortgages. Peddada discloses a API based authentication system for interacting with a user. The motivation for combining these references is that the sensitivity of financial and mortgage related fields would require some level of security layer for user safety.
Galitsky and Peddada do not explicitly disclose processing, by the computing device, the query using natural language processing (NLP) techniques comprising sentiment analysis and normalization, however, ‘567 discloses this limitation: (‘567: p[0364] discloses sentiment analysis as part of the NLP techniques; p[0017] disclose tokenizing new utterance strings as individual words, word portions or character sets, which are forms of normalization).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose the sentiment analysis techniques from ‘567 into the combination of Gallitsky and Peddada as a predictable use of prior art elements according to their established functions. The motivation would be to enhance the query understanding and response accuracy of the NLP system by accounting for emotional tone or user sentiment, a well understood objective in the field of user interaction and virtual assistant systems. Furthermore, incorporating sentiment would have constituted a common enhancement to improve the responsiveness and relevance of the assistant’s behavior in view of the user’s emotional state, especially in a financial and real estate context where tone can affect prioritization or escalation.
Galitsky, Peddada and ‘567 does not explicitly disclose wherein the account history comprises a current address of the user; and accessing, by the computing device, housing market data for a geographic division using the current address of the user from the account history. However, Brock discloses wherein the account history comprises a current address of the user (Brock: p[0037-0040] discloses that an owner’s name and address may be stored in a database for subscribers (i.e., users));
and accessing, by the computing device, housing market data for a geographic division using the current address of the user from the account history (Brock: p[0083-0085] the user interface system has options for selecting real-estate related information and may display them within a desired geographic location, owned by particular people, comparison of monetary values etc. this is based on the users account interests);
It would have been obvious to one of ordinary skill in the art to modify Galitsky’s method by incorporating Brock’s teachings by including a user’s current address in the account information and accessing housing market data tied to geographic division and provide it as input to the computing device. The suggestion for doing so is to enhance the personalization and relevance of the query responses as address information is a well-known component of financial and property-related datasets and geographic specificity is a routine consideration in real estate and mortgage decision-making systems. Therefore, the combination of Galitsky in view of Brock would be a simple addition that would yield predictable results and does not require undue experimentation.
The combination of Galitsky, Peddada, ‘567 and Brock do not disclose detecting, by the computing device, a change to the housing market data for the geographic division; and
and providing, by the computing device, a notification identifying the change to the housing market data to the user device. However, Satterfield discloses detecting, by the computing device, a change to the housing market data for the geographic region (Satterfield: Abstract, Col 4 lines 9-16 Col 10 lines 44-67, teaches a computer device that periodically monitors databases deemed necessary by the user, this data may be housing market data, mortgage data etc.);
providing, by the computing device, a notification identifying the change to the housing market data to for the geographic division to the user device (Satterfield: Abstract, Col 10 lines 44-67, teaches the system may send notifications to the user if the underlying financial data changes).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Galitsky, Peddada, ‘567 and Brocks combined system to detect changes in the housing market data for a geographic region. Galitsky already discloses a method for receiving queries relating to mortgages. Brock discloses that providing housing-related financial information relevant to mortgage queries. Peddada discloses an API based authentication system for interacting with a user. Satterfield discusses monitoring and notifying about credit, financial data, mortgage interest rates and other pieces of data directly tied to the housing market to discuss the context of mortgage related queries. The motivation to combine these systems would be to provide real-time accurate answering capabilities in Galitsky’s system which is already focused on providing mortgage advice to the user and further bolstering it with current data analysis so a user can make an informed decision.
Regarding Claim 3:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 1, wherein the one or more natural language processing (NLP) techniques comprise tokenization (Galitsky: Fig. 2 and Fig. 3 display tokenization), sentiment analysis, normalization, named entity recognition (Galitsky: Col 7 lines 49-65 and Col 12 lines 10-25 can identify words and entities such as nouns, also see Fig. 5 503), and/or dependency parsing (Galitsky: Col 7- Col 8 and Fig. 2-3 disclose/display dependency parsing by analyzing the structure of sentences and identifying relationships between words).
Regarding Claim 4:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 1, wherein the virtual assistant application comprises a machine learning model (Galitsky: Fig. 1 Machine-Learning Model 122).
Regarding Claim 5:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 4, wherein the machine learning model is a neural network (Galitsky: Col 5 lines 1-30 disclose neural networks).
Regarding Claim 6:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 1, wherein the housing market data comprises a mortgage interest rate (Galitsky: Col 10 lines 58-67 discloses the user asking question related to interest rates, additionally it is assumed that since the main purpose of this model is to answer questions regarding mortgages, that market data, especially market data on something so vital as interest rates would be necessarily included in the document/training repositories)
Regarding Claim 7:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 1, wherein the query history comprises a record of questions and responses between the user and the virtual assistant application (Galitsky: Fig. 6 database 604 can include question intent prefixes and mental verbs from previous interactions, additionally Col 5 lines 15-30 discloses that the output may be stored in a database, which would include the record of input and output from an interaction).
Regarding Claim 8:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the method of claim 1, wherein the financial information associated with the user comprises one or more bank account statements, a credit history, one or more proof of income documents, and/or one or more tax returns (Galitsky: Col 21 line 62 – Col 22 line 30 Machine learning model is trained on user data such as banking account information, transactional history, credit score, credit report, tax documents, loan applications etc.).
Regarding Claim 9:
Claim 9 has been analyzed with regard to claim 1 (see rejection above) and
is rejected for the same reasons of obviousness used above.
It is noted that Galitsky discloses a system at least in Fig. 12.
Regarding Claim 11:
Claim 11 has been analyzed with regard to claim 3 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 12:
Claim 12 has been analyzed with regard to claim 4 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 13:
Claim 13 has been analyzed with regard to claim 5 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 14:
Claim 14 has been analyzed with regard to claim 8 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 15:
Claim 15 has been analyzed with regard to claims 1 and 9 (see rejection above) and
is rejected for the same reasons of obviousness used above.
It is noted Galitsky discloses a non-transitory computer-readable medium storing a set of instructions at least at Col 26:33-48.
Regarding Claim 17:
Claim 17 has been analyzed with regard to claims 3 and 11 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 18:
Claim 18 has been analyzed with regard to claims 4 and 12(see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 19:
Claim 19 has been analyzed with regard to claims 5 and 13 (see rejection above) and
is rejected for the same reasons of obviousness used above.
Regarding Claim 20:
The combination of Galitsky, Peddada, Brock, ‘567 and Satterfield further disclose the non-transitory computer-readable medium of claim 15, wherein the housing market data comprises one or more of an average price of a single family home in the geographic division, an average price per square foot for residential real estate in the geographic division, and a vacancy rate of residential real estate (Brock: p[0022], p[0039] disclose the system includes comparisons of actual rental values and market standard values for properties including single family homes; p[0085] discloses comparison of cost and size per square foot).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Jibowu (US 10,565,655) discloses a system which includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for providing customized financial advice. The system may receive transaction data for a transaction associated with a customer and satisfaction data associated with the transaction. Based on the received transaction data and satisfaction data, the system may update a financial state of the customer and a financial policy for determining one or more actions to take in order to maximize a cumulative reward associated with the customer. The system may determine and output a recommended action based on the updated financial policy and customer financial state.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IAN SCOTT MCLEAN whose telephone number is (703)756-4599. The examiner can normally be reached "Monday - Friday 8:00-5:00 EST, off Every 2nd Friday".
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654