DETAILED ACTION
Status of Application
This action is a Non-Final Rejection. This action is in response to the application filed on August 22, 2024.
Claims 1-14 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 18, 2025 has been considered by the examiner.
Claim Objections
Claim 2 is objected to for the following reason: Claim 2 recites “wherein. in step (a)….” It appears that the period after “wherein” is a typographical error. Appropriate correction is required.
Claim Interpretation
Applicant should be aware that there is claim language that does not serve to differentiate the claims from the prior art and/or provide an additional element that can be a consideration for eligibility1. See MPEP 2103(c).
Contingent Limitations
Contingent limitations are generally not given patentable weight. For example, if a claim states that a step occurs if a condition is met, the broadest reasonable interpretation of the claim does not require that the contingent step occurs because the condition may not be satisfied. System claims differ in that even if a condition that is required to perform a function is not met, the structure for performing the contingent limitation is given patentable weight. See MPEP 2111.04(II); see also Ex parte Schulhauser, Appeal 2013-007847 (PTAB April 28, 2016).
The following limitations are contingent:
Claim 7: step (c) further includes (c+1) comparing a predicted exchange rate value in the calculated exchange rate prediction information with the target exchange rate value, and determining that the exchange rate prediction information reaches the input target exchange rate value when an amount of change per preset unit of the predicted exchange rate value converges on the target exchange rate value within a preset period
Claim 8: in step (c+1), the predicted exchange rate value in the calculated exchange rate prediction information is compared with the target exchange rate value, and when the amount of change per preset unit of the predicted exchange rate value does not converge on the target exchange rate value within the preset period, the exchange rate prediction information is determined that does not reach the input target exchange rate value
Claim 10: in step (d), when a correction value for the target exchange rate value is received from the user terminal, the modified target exchange rate value is reflected, and steps (a) to (c) are performed again
Intended Use
Intended use language is generally not given patentable weight. See MPEP 2114(II) ("A claim containing a 'recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus’ if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).”); see also MPEP 2103(C). Examples of claim limitations that are often found to precede intended use include “adapted to,” “capable of,” “sufficient to,” “whereby,” and “for.”
The following limitations include intended use limitations:
Claims 1 and 14: (a) providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value
Claims 1 and 14: (c) inputting current structured data into the learned model to calculate exchange rate prediction information
Claim 3: in step (a), a selection input for multi artificial intelligence models is received from the user terminal, and at least one structured data to be learned by each artificial intelligence model is selected for each artificial intelligence model
Claim 11: step (a) further includes additionally selecting unstructured data from the user terminal, extracting the unstructured data via web crawling by the server, and inputting the unstructured data into an LSTM model to extract structured data, and the extracted structured data is used to learn the artificial intelligence model selected by the user terminal together with economic real variables, economic derived variables, and psychological derived data
Nonfunctional Descriptive Material
Nonfunctional descriptive material is generally not given patentable weight. See MPEP 2111.05. Any difference related merely to the meaning and information conveyed through labels (i.e., the type of the item) which does not explicitly alter or impact the steps of the method is nonfunctional descriptive material and does not patentably distinguish the claimed invention from the prior art in terms of patentability.
The following limitations include nonfunctional descriptive material:
Claims 1 and 14: and receiving a correction value for the target exchange rate value from the user terminal
Claim 2: in step (a), the structured data includes economic real variables, economic derived variables, and psychological derived data
Claim Rejections - 35 USC § 112(b)
The following is a quotation 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. The following are examples of claim language that is indefinite:
Claims 1 and 14: “(a) providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value.” It’s not clear whether the received input includes a type or category of structured data or whether the received input includes the structured data. Additionally, it’s not clear whether the input includes an artificial intelligence model. Additionally, “a country to be exchanged” is not clear.
Claims 1 and 14: “(b) performing learning by inputting the input structured data into the type of artificial intelligence model among multi artificial intelligence models.” It is unclear what is meant by “performing learning.” It appears that this may mean that an AI model is being trained, but it is not clear. Additionally, it is not clear what is meant by “multi artificial intelligence models.” It appears that this may be intended to mean multiple artificial intelligence models.
Claims 1 and 14: “and receiving a correction value for the target exchange rate value from the user terminal.” It is unclear what is meant by “a correction value.” For example, it is not clear whether the correction value is a new target exchange rate value or some other value related to the target exchange rate value.
Claim 2: “in step (a), the structured data includes economic real variables, economic derived variables, and psychological derived data.” It is not clear whether these are the types of data that are selected to be used (i.e., types/categories are selected) or whether these are the actual data that are inputted (i.e., data within those types/categories are selected).
Claim 3: “in step (a), a selection input for multi artificial intelligence models is received from the user terminal, and at least one structured data to be learned by each artificial intelligence model is selected for each artificial intelligence model.” The limitation of “at least one structured data to be learned by each artificial intelligence model” is unclear. For example, it is not clear if this relates to training a model, applying a model, or something else.
Claim 4: “in step (b), the artificial intelligence model uses the selected structured data as an input value and performs learning according to a preset algorithm, so that when current structured data is input, the artificial intelligence model is learned to output the exchange rate value for each date.” The limitation of “performs learning” is unclear as to whether it is referring to training the model, applying the model, or something else.
The other claims have similar indefiniteness issues. The claims were examined as best understood.
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 1-14 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-13, which recite a method and, therefore, are directed to the statutory class of process.
Yes, with respect to claim 14, which recites a system and, therefore, is directed to the statutory class of machine or manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. An exchange rate prediction method based on multi artificial intelligence models performed by a server, the exchange rate prediction method comprising:
(a) providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value;
(b) performing learning by inputting the input structured data into the type of artificial intelligence model among multi artificial intelligence models;
(c) inputting current structured data into the learned model to calculate exchange rate prediction information; and
(d) providing the target exchange rate value and the calculated exchange rate prediction information, and receiving a correction value for the target exchange rate value from the user terminal.
2. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein.
in step (a), the structured data includes economic real variables, economic derived variables, and psychological derived data.
3. The exchange rate prediction method based on multi artificial intelligence models according to claim 2, wherein,
in step (a), a selection input for multi artificial intelligence models is received from the user terminal, and at least one structured data to be learned by each artificial intelligence model is selected for each artificial intelligence model.
4. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein,
in step (b), the artificial intelligence model uses the selected structured data as an input value and performs learning according to a preset algorithm, so that when current structured data is input, the artificial intelligence model is learned to output the exchange rate value for each date.
5. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein,
in step (c), the artificial intelligence model receives a selection input for any one artificial intelligence model among multi artificial intelligence models each of which completed learning using different learning data and machine learning methods.
6. The exchange rate prediction method based on multi artificial intelligence models according to claim 5, wherein
the artificial intelligence model is learned according to any one of the learning methods of XG Boost, Decision Tree, Logistic Regression, Random Forest, Support Vector Classifier, LSTM, and Ensemble Bagging.
7. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein
step (c) further includes (c+1) comparing a predicted exchange rate value in the calculated exchange rate prediction information with the target exchange rate value, and determining that the exchange rate prediction information reaches the input target exchange rate value when an amount of change per preset unit of the predicted exchange rate value converges on the target exchange rate value within a preset period.
8. The exchange rate prediction method based on multi artificial intelligence models according to claim 7, wherein,
in step (c+1), the predicted exchange rate value in the calculated exchange rate prediction information is compared with the target exchange rate value, and when the amount of change per preset unit of the predicted exchange rate value does not converge on the target exchange rate value within the preset period, the exchange rate prediction information is determined that does not reach the input target exchange rate value.
9. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein,
in step (d), a predicted exchange rate-time graph is generated based on the exchange rate prediction information output by the artificial intelligence model, the predicted exchange rate-time graph and an actual exchange rate-time graph are displayed by being overlapped, and a predicted exchange rate-time graph is created separately for each artificial intelligence model selected by the user terminal to provide information on performance of each artificial intelligence model to the user terminal.
10. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein,
in step (d), when a correction value for the target exchange rate value is received from the user terminal, the modified target exchange rate value is reflected, and steps (a) to (c) are performed again.
11. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein
step (a) further includes additionally selecting unstructured data from the user terminal, extracting the unstructured data via web crawling by the server, and inputting the unstructured data into an LSTM model to extract structured data, and the extracted structured data is used to learn the artificial intelligence model selected by the user terminal together with economic real variables, economic derived variables, and psychological derived data.
12. The exchange rate prediction method based on multi artificial intelligence models according to claim 11, wherein
the unstructured data includes at least one of news articles, Korea Monetary Policy Committee meeting records, and US FOMC meeting records, and
in step (a), the server performs a preprocessing process in which unstructured data is input into a natural language processing model, and sentences or words extracted from the unstructured data is vectorized.
13. The exchange rate prediction method based on multi artificial intelligence models according to claim 1, wherein
the exchange rate prediction information is exchange rate prediction information for a period within a week.
14. An exchange rate prediction server based on multi artificial intelligence models, comprising:
a memory storing a program for performing an exchange rate prediction method based on multi artificial intelligence models; and
a processor for executing the program,
wherein the method includes:
providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value,
performing learning by inputting the input structured data into the type of artificial intelligence model among multi artificial intelligence models,
inputting current structured data into the learned model to calculate exchange rate prediction information, and
providing the target exchange rate value and the calculated exchange rate prediction information, and receiving a correction value for the target exchange rate value from the user terminal.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to predicting exchange rates. This type of method of organizing human activity is a fundamental economic practice because it involves hedging and mitigating risk and a commercial interaction such as sales activities or behaviors and business relations.
The claims also recite mental processes. For example, receiving input from a user terminal involves observation, performing learning and calculating exchange rate prediction information involve evaluation and judgment, and providing a target exchange rate value and the calculated exchange rate prediction information involve judgment and opinion.
Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are 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 1-6, 10, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Remlinger, U.S. Patent Application Publication No. 2020/0250750 A1 and Vollmert et al., U.S. Patent Application Publication Number 2017/0316499 A1.
Claim 1:
Remlinger teaches:
(a) providing a currency exchange service application to a user terminal, and receiving input from the user terminal of the type of structured data and artificial intelligence model to be used for exchange rate prediction, a country to be exchanged, and a target exchange rate value (see at least Remlinger, paragraph 0020 (“The clients 110 include one or more computing devices, including but not limited to, mobile devices (e.g., a smartphone or PDA), tablet computers, laptop computers, desktop computers, and/or other devices capable of running a volatility prediction application.”); paragraph 0024 (“The server 130 receives inputs from users 170 via clients 110 over the network 150. Examples of inputs received by the server 130 include, but are not limited to, receiving user input data related to user login information, such as a username, a password, and the like. User input data includes data related to inputs from a user that specify currencies for currency pairs for which the user desires prediction of volatility at a time period in the future. The server 130, via processor 236, may be configured to provide the user inputs to one or more other sub-components of the server 130, such as the technical indicator engine 240, the event data engine 242, the sentimental analysis engine 244, the artificial intelligence trained volatility prediction engine 246. … Examples of data received by the server 130 include, but are not limited to, exchange rate data for various currency pairs, trade execution data for various trader users or traders, trade execution data from various trading exchanges, economic event data, user input data.”); paragraph 0036 (“The server 130 may receive each currency of the currency pair as user input from a user, such as a user 170. In some implementations, the server 130 may be configured to receive each of the currencies of the currency pair via a graphical user interface (GUI) of an instance of the volatility prediction application 222 being executed on a client device of the user, such as a client device 110 of the user 170.”)).
(b) performing learning by inputting the input structured data into the type of artificial intelligence model among multi artificial intelligence models (see at least Remlinger, paragraph 0027 (“In some implementations, the technical indicator engine 240 may be configured with one or more artificial intelligence models trained to identify whether volatility may increase, decrease, or remain within a threshold range over a certain time period in the future. In some implementations, the artificial intelligence models may be configured to receive as inputs the encoded data generated based on the technical indicators.”)).
(c) inputting current structured data into the learned model to calculate exchange rate prediction information (see at least Remlinger, paragraph 0027 (“In some implementations, the technical indicator engine 240 may be configured with one or more artificial intelligence models trained to identify whether volatility may increase, decrease, or remain within a threshold range over a certain time period in the future. In some implementations, the artificial intelligence models may be configured to receive as inputs the encoded data generated based on the technical indicators.”); paragraph 0043).
(d) providing the target exchange rate value and the calculated exchange rate prediction information, (see at least Remlinger, paragraph 0043 (“At step 308, the server 130, via the volatility prediction engine 246, predicts one or more exchange rates for the currency pair. The server 130, via the volatility prediction engine 246, may be configured to predict the one or more exchange rates for the currency pair based on the predicted volatility for the currency pair. In some implementations, the server 130, via the volatility prediction engine 246, may be configured to predict one or more exchange rates for the currency pair at the end of the time period received from the user. The server 130, via the volatility prediction engine 246, may be configured to predict the one or more exchange rates for the currency pair based on a current exchange rate for the currency pair and the predicted volatility.”); paragraph 0044 (“At step 309, the server 130 may cause the one or more exchange rates to be displayed on the graphical user interface displayed on a computing device. For example, the server 130 may cause the one or more exchange rates to be displayed on the graphical user interface displayed on the client device 110 of the user 170. The graphical user interface may be a graphical user interface of the volatility prediction application being executed on the computing device of the user, such as client 110 of user 170.”)).
Remlinger does not explicitly teach, but Vollmert, however, does teach:
and receiving a correction value for the target exchange rate value from the user terminal (see at least Vollmert, paragraph 0054 (“As adjustments are made the foreign currency exchange rate in adjustment area 610, the exchange rate displayed in simulated exchange rate area 622 may change accordingly. The impact of the simulated exchange rate is displayed below. In some embodiments, the simulated financial data is displayed in both the foreign currency and equivalent base currency according to the simulated exchange rate. In some embodiments, the difference (in base currency) between the reference financial data and the simulated financial data is also displayed. After setting the simulated foreign currency exchange rate (or rates, for multiple time frames) for a specific foreign currency, the user may additionally set the simulated foreign currency exchange rate(s) for additional foreign currencies selected previously. After making the desired foreign currency exchange rate changes, the user may then view and analyze the resulting simulated data.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Vollmert’s method of adjusting the desired foreign currency exchange rate with Remlingers method of predicting exchange rates between two currencies at the end of a time period. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of determining different scenarios in order to assist in making a decision.
Claim 2:
Remlinger further teaches:
in step (a), the structured data includes economic real variables, economic derived variables, and psychological derived data (see at least Remlinger, paragraph 0020 (“The clients 110 include one or more computing devices, including but not limited to, mobile devices (e.g., a smartphone or PDA), tablet computers, laptop computers, desktop computers, and/or other devices capable of running a volatility prediction application.”); paragraph 0024 (“The server 130 receives inputs from users 170 via clients 110 over the network 150. Examples of inputs received by the server 130 include, but are not limited to, receiving user input data related to user login information, such as a username, a password, and the like. User input data includes data related to inputs from a user that specify currencies for currency pairs for which the user desires prediction of volatility at a time period in the future. The server 130, via processor 236, may be configured to provide the user inputs to one or more other sub-components of the server 130, such as the technical indicator engine 240, the event data engine 242, the sentimental analysis engine 244, the artificial intelligence trained volatility prediction engine 246. … Examples of data received by the server 130 include, but are not limited to, exchange rate data for various currency pairs, trade execution data for various trader users or traders, trade execution data from various trading exchanges, economic event data, user input data.”); paragraph 0036 (“The server 130 may receive each currency of the currency pair as user input from a user, such as a user 170. In some implementations, the server 130 may be configured to receive each of the currencies of the currency pair via a graphical user interface (GUI) of an instance of the volatility prediction application 222 being executed on a client device of the user, such as a client device 110 of the user 170.”)).
Claim 3:
Remlinger further teaches:
in step (a), a selection input for multi artificial intelligence models is received from the user terminal, and at least one structured data to be learned by each artificial intelligence model is selected for each artificial intelligence model (see at least Remlinger, paragraph 0020 (“The clients 110 include one or more computing devices, including but not limited to, mobile devices (e.g., a smartphone or PDA), tablet computers, laptop computers, desktop computers, and/or other devices capable of running a volatility prediction application.”); paragraph 0024 (“The server 130 receives inputs from users 170 via clients 110 over the network 150. Examples of inputs received by the server 130 include, but are not limited to, receiving user input data related to user login information, such as a username, a password, and the like. User input data includes data related to inputs from a user that specify currencies for currency pairs for which the user desires prediction of volatility at a time period in the future. The server 130, via processor 236, may be configured to provide the user inputs to one or more other sub-components of the server 130, such as the technical indicator engine 240, the event data engine 242, the sentimental analysis engine 244, the artificial intelligence trained volatility prediction engine 246. … Examples of data received by the server 130 include, but are not limited to, exchange rate data for various currency pairs, trade execution data for various trader users or traders, trade execution data from various trading exchanges, economic event data, user input data.”); paragraph 0036 (“The server 130 may receive each currency of the currency pair as user input from a user, such as a user 170. In some implementations, the server 130 may be configured to receive each of the currencies of the currency pair via a graphical user interface (GUI) of an instance of the volatility prediction application 222 being executed on a client device of the user, such as a client device 110 of the user 170.”)).
Claim 4:
Remlinger further teaches:
in step (b), the artificial intelligence model uses the selected structured data as an input value and performs learning according to a preset algorithm, so that when current structured data is input, the artificial intelligence model is learned to output the exchange rate value for each date (see at least Remlinger, paragraph 0027 (“In some implementations, the technical indicator engine 240 may be configured with one or more artificial intelligence models trained to identify whether volatility may increase, decrease, or remain within a threshold range over a certain time period in the future. In some implementations, the artificial intelligence models may be configured to receive as inputs the encoded data generated based on the technical indicators.”)).
Claim 5:
Remlinger further teaches:
in step (c), the artificial intelligence model receives a selection input for any one artificial intelligence model among multi artificial intelligence models each of which completed learning using different learning data and machine learning methods (see at least Remlinger, paragraph 0027 (“In some implementations, the technical indicator engine 240 may be configured with one or more artificial intelligence models trained to identify whether volatility may increase, decrease, or remain within a threshold range over a certain time period in the future. In some implementations, the artificial intelligence models may be configured to receive as inputs the encoded data generated based on the technical indicators.”); paragraph 0043).
Claim 6:
Remlinger further teaches:
the artificial intelligence model is learned according to any one of the learning methods of XG Boost, Decision Tree, Logistic Regression, Random Forest, Support Vector Classifier, LSTM, and Ensemble Bagging (see at least Remlinger, paragraph 0032 (decision trees)).
Claim 10:
Remlinger does not explicitly teach, but Vollmert, however, does teach:
in step (d), when a correction value for the target exchange rate value is received from the user terminal, the modified target exchange rate value is reflected, and steps (a) to (c) are performed again (see at least Vollmert, paragraph 0054 (“As adjustments are made the foreign currency exchange rate in adjustment area 610, the exchange rate displayed in simulated exchange rate area 622 may change accordingly. The impact of the simulated exchange rate is displayed below. In some embodiments, the simulated financial data is displayed in both the foreign currency and equivalent base currency according to the simulated exchange rate. In some embodiments, the difference (in base currency) between the reference financial data and the simulated financial data is also displayed. After setting the simulated foreign currency exchange rate (or rates, for multiple time frames) for a specific foreign currency, the user may additionally set the simulated foreign currency exchange rate(s) for additional foreign currencies selected previously. After making the desired foreign currency exchange rate changes, the user may then view and analyze the resulting simulated data.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Vollmert’s method of adjusting the desired foreign currency exchange rate with Remlingers method of predicting exchange rates between two currencies at the end of a time period. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of determining different scenarios in order to assist in making a decision.
Claim 13:
Remlinger does not explicitly teach:
the exchange rate prediction information is exchange rate prediction information for a period within a week.
Remlinger, however, teaches:
Exchange rate prediction for a time period in the future. See at least Remlinger, paragraph 0024 (“User input data includes data related to inputs from a user that specify currencies for currency pairs for which the user desires prediction of volatility at a time period in the future.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate this feature with Remlingers method of predicting exchange rates between two currencies at the end of a time period. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of predicting an exchange rate in a period of time that would be useful. Remlinger teaches that the exchange rate is predicted for a time period in the future. Although it does not specify that the time period is a week, it would be obvious to predict the exchange rate for any useful time period, including a week, so that a user can rely on the prediction in an amount of time such as a week.
Claim 14:
Claim 14 is rejected using the same rationale that was used for the rejection of claim 1.
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Remlinger, U.S. Patent Application Publication No. 2020/0250750 A1; Vollmert et al., U.S. Patent Application Publication Number 2017/0316499 A1; and Biggs et al., U.S. Patent Application Publication Number 2024/0104555 A1.
Claim 7:
Remlinger does not explicitly teach, but Biggs, however, does teach:
step (c) further includes (c+1) comparing a predicted exchange rate value in the calculated exchange rate prediction information with the target exchange rate value, and determining that the exchange rate prediction information reaches the input target exchange rate value when an amount of change per preset unit of the predicted exchange rate value converges on the target exchange rate value within a preset period (see at least Biggs, paragraph 0180 (“For example, a desired minimum exchange rate specified in a commission or policy profile may be compared to one or more predictions of future exchange rate produced by the artificial intelligence engine 700, and a qualitative assessment may be produced (by the artificial intelligence engine 700 or other portions of the platform 100) based on that comparison.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Biggs’ method of comparing a desired exchange rate with a predicted exchange rate with Remlingers method of predicting exchange rates between two currencies at the end of a time period. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of producing a qualitative assessment. See Biggs, paragraph 0180.
Claim 8:
Remlinger does not explicitly teach, but Biggs, however, does teach:
in step (c+1), the predicted exchange rate value in the calculated exchange rate prediction information is compared with the target exchange rate value, and when the amount of change per preset unit of the predicted exchange rate value does not converge on the target exchange rate value within the preset period, the exchange rate prediction information is determined that does not reach the input target exchange rate value (see at least Biggs, paragraph 0180 (“For example, a desired minimum exchange rate specified in a commission or policy profile may be compared to one or more predictions of future exchange rate produced by the artificial intelligence engine 700, and a qualitative assessment may be produced (by the artificial intelligence engine 700 or other portions of the platform 100) based on that comparison.”)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Biggs’ method of comparing a desired exchange rate with a predicted exchange rate with Remlingers method of predicting exchange rates between two currencies at the end of a time period. One of ordinary skill in the art would have been motivated to incorporate this feature for the purpose of producing a qualitative assessment. See Biggs, paragraph 0180.
Note: Claims 9, 11, and 12 are not rejected under 35 U.S.C. 103. Although individual claim features are known in the art, these claims as a whole, as best understood in light of the indefiniteness issues, are not made obvious by the prior art.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Ogunsola et al., U.S. Patent Application Publication Number 2022/0398662 A1. This reference teaches a method of facilitating exchange and transaction of currencies.
Baid, U.S. Patent Application Publication Number 2022/0318903 A1. This reference teaches machine learning based prediction for multiple currency goals.
Email Communications
Per MPEP 502.03, Applicant may authorize email communications by filing Form PTO/SB/439, available at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf, via the USPTO patent electronic filing system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
1 See MPEP 2106.04(d)(2) (“Examiners should keep in mind that in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration. For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.”)