DETAILED ACTION
Status of Claims
This is a final action in reply to the response filed on December 22, 2025.
Claims 1, 11 and 20 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1-20 under 35 USC § 101 is maintained. Please see the Response to Arguments.
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. In adhering to the 2019 PEG, Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-10 falls within statutory class of a process, claims 11-19 falls within statutory class of a machine and clam 20 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, the 2019 PEG flowchart is directed to Step 2. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
receiving, by a microservice, a text message sent from a mobile application, wherein the text message comprises a purchase transaction summary originated from a provider and relayed to the microservice by the mobile application, wherein the microservice comprises a named entity recognition engine, a validator, a natural language processing classifier, and an entry generator;
automatically extracting, by the name entity recognition engine of the microservice, a plurality of named entities from the text message;
automatically evaluating, by the validator of the microservice, based on the plurality of named entities, whether the text message represents a valid expenditure;
responsive to finding that the text message represents a valid expenditure, automatically classifying, by the natural language processing classifier of the microservice, the text message into one of multiple categories; and
automatically generating, by the entry generator of the microservice, an expenditure entry comprising the plurality of named entities and a category to which the text message is classified into.
Claim 11:
memory; one or more hardware processors coupled to the memory;
and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:
receiving, by a microservice, a text message sent from a mobile application, wherein the text message comprises a purchase transaction summary originated from a provider and relayed to the microservice by the mobile application, wherein the microservice comprises a named entity recognition engine, a validator, a natural language processing classifier, and an entry generator;
automatically extracting, by the name entity recognition engine of the microservice, a plurality of named entities from the text message;
automatically evaluating, by the validator the microservice, based on the plurality of named entities, whether the text message represents a valid expenditure;
responsive to finding that the text message represents a valid expenditure, automatically classifying, by the natural language processing classifier of the microservice, the text message into one of multiple categories; and
automatically generating, by the entry generator of the microservice, an expenditure entry comprising the plurality of named entities and a category to which the text message is classified into.
Claim 20:
receiving, by a microservice, a text message sent from a mobile application, wherein the text message comprises a purchase transaction summary originated from a provider and relayed to the microservice by the mobile application, wherein the microservice comprises a named entity recognition engine, a validator, a natural language processing classifier, and an entry generator;
automatically extracting, by the name entity recognition engine of the microservice, a plurality of named entities from the text message;
automatically evaluating, by the validator of the microservice, based on the plurality of named entities, whether the text message represents a valid expenditure;
responsive to finding that the text message represents a valid expenditure, automatically classifying, by the natural language processing classifier of the microservice, the text message into one of multiple categories;
automatically generating, by the entry generator of the microservice, an expenditure entry comprising the plurality of named entities and a category to which the text message is classified into; and
sending, from the microservice, a response to the mobile application, wherein the response comprises an identifier of the expenditure entry, wherein the extracting comprises identifying words in the text message corresponding to predefined entity classes.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial interactions, including advertising, marketing or sales activities or behaviors, business relations. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier is recited at a high level of generality, i.e., as a generic computing and processing system. This memory, one or more hardware processors and the microservices is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. The name entity recognition engine and natural language processing classifier are natural language processing tasks, used a as a tool in its ordinary capacity, to carry out the abstract idea. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier type structure at paragraphs 0057: “the computing system 500 includes one or more processing units 510, 515 and memory 520, 525. In FIG. 5 , this basic configuration 530 is included within a dashed line.” Paragraph 0013: “microservices are small, independently deployable, and loosely coupled software applications that perform specific functions within a larger enterprise application. Each microservice can be developed, deployed, and scaled independently, promoting flexibility, modularity, and easier maintenance. The microservices can communicate through pre-defined APIs and can be written in different programming languages, allowing for agile development and efficient system architecture.” See also figure 5. Paragraph 0016: “Named entity recognition is a natural language processing task that involves identifying and classifying named entities in a given text.” And paragraph 0019: “ the NLP classifier 150, which can use a machine learning (ML) model to classify the text message 104 into one of multiple categories or expense types.”
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-10 and 12-19 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2 and 12 further limit the abstract idea that the text message is one of a plurality of text messages and the expenditure entry is one of a plurality of expenditure entries generated from the plurality of text messages, wherein the method further comprises selecting one or more of the expenditure entries and generating a report comprising the selected one or more of the expenditure entries (a more detailed abstract idea remains an abstract idea). Claims 3 and 13 further limit the abstract idea by sending, from the microservice, a response to the mobile application, wherein the response comprises an identifier of the expenditure entry (a more detailed abstract idea remains an abstract idea). Claims 4 and 14 further limit the abstract idea that the microservice switches from a sleep mode to an active mode after receiving the text message from the mobile application and switch back to the sleep mode after sending the response to the mobile application (a more detailed abstract idea remains an abstract idea). Claims 5 and 15 further limit the abstract idea that the extracting comprises identifying words in the text message corresponding to predefined entity classes (a more detailed abstract idea remains an abstract idea). Claims 6 and 16 further limit the abstract idea by defining a character sequence pattern and a label for at least one entity class (a more detailed abstract idea remains an abstract idea). Claim 7 further limit the abstract idea that the character sequence pattern for the at least one entity class represents multiple consecutive decimal numbers (a more detailed abstract idea remains an abstract idea). Claims 8 and 17 further limit the abstract idea that the evaluating comprises comparing the plurality of named entities with pre-registered entity data (a more detailed abstract idea remains an abstract idea). Claims 9 and 18 further limit the abstract idea that the classifying comprises converting the text message into numerical vectors in a multidimensional space and measuring similarities between the numerical vectors (a more detailed abstract idea remains an abstract idea). And claims 10 and 19 further limit the abstract idea by training a machine learning model using the text message and the category to which the text message is classified into (a more detailed abstract idea remains an abstract idea).The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Certain Methods of Organizing Human Activity such as commercial interactions, including advertising, marketing or sales activities or behaviors, business relations. A machine learning model for training, is used as a tool, in its ordinary capacity, to carry out the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
With regard to the 35 U.S.C. 103 rejections. Applicant’s arguments (Remarks pages 7-10) with respect to claim(s) 1. 11 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please the updated rejection below as necessitated by amendments.
With regard to the 35 U.S.C. 101. Applicant's arguments filed on 12/22/2025 have been fully considered but they are not persuasive. Applicant argues that (1) “Claims do not recite an abstract idea.” (2) “the claims are deemed to recite a judicial exception, which the Applicant does not concede, the alleged exception is integrated into a practical application of the exception” and (3) “the claims as a whole amount to significantly more than the abstract idea itself” (Remarks, pages 10-16).
In response to Applicant’s argument (1). Examiner respectfully disagrees. Claim 1 recites a method for implementing message-based expense capture and classification in real-time by using microservices to receive text messages from a mobile application which is a purchase transaction summary from the provider, the named entity recognition extract a plurality of named entities from the text message, the validator evaluate that the text message is a valid expenditure, if the text message is a valid expenditure, the natural processing classifier, classify it into one of multiple categories and the entry generator, generate an expenditure entry with the named entities and category as described in Applicant's disclosure in paragraph 0008 " Enterprise EM solutions aim to streamline and automate the processes of managing business expenses, travel bookings, and invoices." Therefore, claim 1 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of Mental Processes, concepts performed in the human mind including observations(purchase transaction summary originated from a provider, extracting the plurality of named entities), evaluation(valid expenditure?), judgement (classifying into one of multiple categories) and opinion (expenditure entry with named entity and category) and Certain Methods of Organizing Human Activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, business relations such as purchase transaction summaries, expenditure entries. Claim 3 of Example 47 was found eligible because the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement described in the background. In contrast, as explained above, claim 1 limitations recite a mental process. The same rationale applies to claims 11 and 20.
In response to Applicant’s argument (2). Examiner respectfully disagrees. Examiner notes that the claims does not describe the improvement described in the specification such as “capture expenditures made on digital payment platforms” nor “seamless integrations with heterogenous digital platforms” (Applicant’s disclosure paragraph 0054-0055). In addition, per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting. The recited The memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier, merely links the abstract idea to a computer environment. In this way, the memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claim 1 uses the memory, one or more hardware processors, the microservices, the named entity recognition engine and natural language processing classifier as a tool, in its ordinary capacity, to carry out the abstract idea. The name entity recognition engine and natural language processing classifier are natural language processing tasks, used a as a tool in its ordinary capacity, to carry out the abstract idea. Applicant’s disclosure describe in paragraph 0013, the definition of microservices: “microservices are small, independently deployable, and loosely coupled software applications that perform specific functions within a larger enterprise application. Each microservice can be developed, deployed, and scaled independently, promoting flexibility, modularity, and easier maintenance. The microservices can communicate through pre-defined APIs and can be written in different programming languages, allowing for agile development and efficient system architecture.” The microservices are used a as a tool in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field of real-time expense capture and classification. Further, a processor configured to cause receiving/determining/transmitting data to a device is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The same rationale applies to claims 11 and 20.
In response In response to Applicant’s argument (3). Examiner respectfully disagrees. Please see the response to Applicant’s argument (2) and the rejection above. In addition, the issues of patentability under 35 U.S.C. §§ 102 and 103 should not be conflated with those under 35 U.S.C. § 101. As explained in Diamond v. Diehr, 450 U.S. 175, 209 U.S.P.Q. The same rationale applies to claims 11 and 20. The rejection is maintained.
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.
Claims 1-2, 9-12 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Velmurugan et al., Expense Manager Application, Journal of Physics: Conference Series, 171, 2020, hereinafter “Velmurugan” in both view of Uyanahewa et al., WONGA: The Future of Personal Finance Management– A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking, 2023 4th International Conference for Emerging Technology (INCET) Belgaum, India. May 26-28, 2023, hereinafter “Uyanahewa” and Wang et al., (US 2021/0073735 A1) hereinafter “Wang”.
Claim 1:
Velmurugan as shown discloses a computer-implemented method for implementing message-based expense capture and classification in real-time, the method comprising:
receiving, by a microservice, a text message sent from a mobile application, wherein the text message comprises a purchase transaction summary originated from a provider and relayed to the microservice by the mobile application (page 3: “We need to give SMS read permission to the app since the whole idea of the app revolves around the transactions made online for which you get a message after any transaction done” and Figure 1: “Expense Tracker Architecture”);
Velmurugan teaches in Figure 1 “Display List of Transactions made” and “Transactions Can Be Viewed as Pie Chart Form”, page 5 describes the Expense Tracker – SMS Reading and page 1: “The features of the app are designed in a way to help you for better finance management planning so that you can keep track of , analyse and optimize your budget or spending’s. In this application we are also going to collect user’s data with authenticated permissions and analyse and study their pattern expenses in certain category or by distinct kinds of spending that can be used for studying market trends. These analysis patterns can be derived using some data mining techniques such as clustering, classification and association.” Velmurugan is silent with the following limitations. However, Uyanahewa in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
automatically extracting, by the named entity recognition engine of the microservice, a plurality of named entities from the text message (page 2, “the Named Entity Recognition (NER) model was used to identify key expense details from the extracted text”);
automatically evaluating, by the validator of the microservice, based on the plurality of named entities, whether the text message represents a valid expenditure; responsive to finding that the text message represents a valid expenditure, automatically classifying, by the natural language processing classifier of the microservice, the text message into one of multiple categories; (Page 1: “to develop a smart personal finance management mobile application that utilizes modern technologies such as machine learning, Natural Language Processing (NLP),” see also page 3: “Artificial Neural Networks (ANN) were chosen as the training algorithm for all classifications used in this process. The first classification performed was bank SMS binary classification, which identified whether a read SMS text was transaction-related or not. The second classification was transaction information extraction. In creating the output labels for the classification, the dataset was utilized, and commonly performed bank transactions such as "ATM Withdrawal", "Transfer Debit", "POS/ATM Transaction", "Online Transfer Debit", and "PURCHASE" were selected. Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns.”);
and automatically generating, by the entry generator of the microservice, an expenditure entry comprising the plurality of named entities and a category to which the text message is classified into (page 3: B. Automated Expense Classifier: “With the aid of machine learning algorithms, this component analyses expense data and automatically classifies them into the appropriate expense categories” and “Once the expense details were extracted, they were saved in a firebase database after encryption.”);
Both Velmurugan and Uyanahewa teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Uyanahewa teaches in the Abstract “This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Uyanahewa would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Uyanahewa to the teaching of Velmurugan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as automatically extracting, by the named entity recognition engine, a plurality of named entities from the text message; automatically evaluating, by the validator, based on the plurality of named entities, whether the text message represents a valid expenditure; responsive to finding that the text message represents a valid expenditure, automatically classifying, by the natural language processing classifier, the text message into one of multiple categories; and automatically generating, by the entry generator, an expenditure entry comprising the plurality of named entities and a category to which the text message is classified into, into similar systems. Further, as noted by Uyanahewa “In-depth analysis by Wonga financial application research led to the recommendation of an intelligent solution in the form of a mobile application that automates personal finance management with minimal user effort, simplifies money management complexities, and helps people manage their finances more effectively.” (Uyanahewa, page 6, Conclusion).
Velmurugan in view of Uyanahewa as explained above teaches a message-based expense capture and classification. Velmurugan in view of Uyanahewa is silent with regard of using a microservices. However, Wang in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
by a microservices, wherein the microservice comprises a named entity recognition engine, a validator, a natural language processing classifier, and an entry generator of the microservices, (Wang use microservices for generating an expense description as shown in Figure 2, ¶ 0149: “The techniques described above may be encapsulated into a microservice, according to one or more embodiments. In other words, a microservice may trigger a notification (into the microservices manager for optional use by other plugged in applications, herein referred to as the “target” microservice) based on the above techniques and/or may be represented as a GUI block and connected to one or more other microservices. The trigger condition may include absolute or relative thresholds for values, and/or absolute or relative thresholds for the amount or duration of data to analyze, such that the trigger to the microservices manager occurs whenever a plugged-in microservice application detects that a threshold is crossed. For example, a user may request a trigger into the microservices manager when the microservice application detects a value has crossed a triggering threshold.” See also ¶ 0050 which describes the trigger: “An expense trigger 138 is a codified set of rules and/or a set of automatically learned patterns that captures one or more conditions for identifying expenses associated with one or more employees' target activity. An expense identified by an expense trigger may be an expense for which .” And an expense trigger related to credit cards ¶ 0054: “The expense trigger 138 may determine that a particular credit card charge is associated (e.g., corresponds in time and/or geographic location) with an employee's relevant activity.” ¶ 0144-0154 describes the uses of microservices “Microservices provide flexibility in managing and building applications.”);
Both Velmurugan and Wang teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Wang teaches in the Abstract “generating an expense report.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teaching of Velmurugan in view of Uyanahewa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the use of microservices into similar systems. Further, as noted by Wang “Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications.” “the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output.” (Wang, ¶ 0146 and 0154).
Claim 11:
The limitations of claim 11 encompasses substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 11 that differ from claim 1.
Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Uyanahewa teaches in the Abstract: “All these smart solutions are bundled up in the “Wonga” mobile application to help users make better financial decisions to achieve personal financial success.” Velmurugan in view of Uyanahewa is silent with regard to the following limitations. However, Wang in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
memory; one or more hardware processors coupled to the memory; and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising (Figure 6: );
Both Velmurugan and Wang teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Wang teaches in the Abstract “generating an expense report.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teaching of Velmurugan in view of Uyanahewa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as memory; one or more hardware processors coupled to the memory; and one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising into similar systems. Further, as noted by Wang “Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications.” “the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output.” (Wang, ¶ 0146 and 0154).
Claims 2 and 12:
Velmurugan as shown discloses the following limitations:
wherein the text message is one of a plurality of text messages (page 3: “We need to give SMS read permission to the app since the whole idea of the app revolves around the transactions made online for which you get a message after any transaction done” and Figure 1: “Expense Tracker Architecture”);
Velmurugan teaches in Figure 1, that the transactions can be view as a pie chart form. Velmurugan is silent with the following limitations. However, Uyanahewa in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
and the expenditure entry is one of a plurality of expenditure entries generated from the plurality of text messages (also page 3: “Artificial Neural Networks (ANN) were chosen as the training algorithm for all classifications used in this process. The first classification performed was bank SMS binary classification, which identified whether a read SMS text was transaction-related or not. The second classification was transaction information extraction. In creating the output labels for the classification, the dataset was utilized, and commonly performed bank transactions such as "ATM Withdrawal", "Transfer Debit", "POS/ATM Transaction", "Online Transfer Debit", and "PURCHASE" were selected. Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns.”);
Both Velmurugan and Uyanahewa teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Uyanahewa teaches in the Abstract “This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Uyanahewa would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Uyanahewa to the teaching of Velmurugan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the expenditure entry is one of a plurality of expenditure entries generated from the plurality of text messages into similar systems. Further, as noted by Uyanahewa “In-depth analysis by Wonga financial application research led to the recommendation of an intelligent solution in the form of a mobile application that automates personal finance management with minimal user effort, simplifies money management complexities, and helps people manage their finances more effectively.” (Uyanahewa, page 6, Conclusion).
Velmurugan in view of Uyanahewa is silent with regard to the following limitations. However, Wang in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
wherein the method further comprises selecting one or more of the expenditure entries and generating a report comprising the selected one or more of the expenditure entries (¶ 0088: “if the expense description is approved (Operation 218), or no user approval is required, the system generates an expense report (Operation 222). The system may generate the expense report responsive to user input corresponding to a user instruction to generate the expense report. ):
Both Velmurugan and Wang teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Wang teaches in the Abstract “generating an expense report.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teaching of Velmurugan in view of Uyanahewa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as selecting one or more of the expenditure entries and generating a report comprising the selected one or more of the expenditure entries into similar systems. Further, as noted by Wang “Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications.” “the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output.” (Wang, ¶ 0146 and 0154).
Claims 9 and 18:
Uyanahewa teaches in page 3: “The data were transformed into a numerical representation using Count Vectorizer.” Velmurugan in view of Uyanahewa is silent with regard to the following limitations. However, Wang in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
wherein the classifying comprises converting the text message into numerical vectors in a multidimensional space and measuring similarities between the numerical vectors (¶ 0042: “A machine learning engine 124 may transform inputs into feature vectors that describe one or more properties (“features”) of the inputs. The machine learning engine 124 may label, classify, and/or categorize the inputs based on the feature vectors. Alternatively or additionally, a machine learning engine 124 may use clustering (also referred to as cluster analysis) to identify commonalities in the inputs. The machine learning engine 124 may group (i.e., cluster) the inputs based on those commonalities. The machine learning engine 124 may use hierarchical clustering, k-means clustering, and/or another clustering method or combination thereof. […] a machine learning engine 124 may include a support vector machine. A support vector machine represents inputs as vectors. The machine learning engine 124 may label, classify, and/or categorizes inputs based on the vectors. The coordinates of the vectors and corresponding boundaries between different hyperplanes may be adjusted as machine learning proceeds.”);
Both Velmurugan and Wang teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Wang teaches in the Abstract “generating an expense report.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teaching of Velmurugan in view of Uyanahewa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the classifying comprises converting the text message into numerical vectors in a multidimensional space and measuring similarities between the numerical vectors into similar systems. Further, as noted by Wang “Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications.” “the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output.” (Wang, ¶ 0146 and 0154).
Claims 10 and 19:
Velmurugan is silent with the following limitations. However, Uyanahewa in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
further comprising training a machine learning model using the text message and the category to which the text message is classified into (page 3: To analyze bank transactions received via bank SMS, two models were trained for different classifications of bank SMS. The dataset was manually built with relevant labels to train the model. After collecting the SMS data, it was divided into two separate CSV files (SMS and spam) and combined in the data pre-processing stage in model training. Two text pre-processing methods were employed: tokenizing and added padding (Max Length = 100). Artificial Neural Networks (ANN) were chosen as the training algorithm for all classifications used in this process. The first classification performed was bank SMS binary classification, which identified whether a read SMS text was transaction-related or not. The second classification was transaction information extraction. In creating the output labels for the classification, the dataset was utilized, and commonly performed bank transactions such as "ATM Withdrawal", "Transfer Debit", "POS/ATM Transaction", "Online Transfer Debit", and "PURCHASE" were selected. Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns. For extracting the amount and transaction type, two patterns were employed, and the information was extracted accordingly.”);
Both Velmurugan and Uyanahewa teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Uyanahewa teaches in the Abstract “This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Uyanahewa would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Uyanahewa to the teaching of Velmurugan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as further comprising training a machine learning model using the text message and the category to which the text message is classified into, into similar systems. Further, as noted by Uyanahewa “In-depth analysis by Wonga financial application research led to the recommendation of an intelligent solution in the form of a mobile application that automates personal finance management with minimal user effort, simplifies money management complexities, and helps people manage their finances more effectively.” (Uyanahewa, page 6, Conclusion)
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Velmurugan et al., Expense Manager Application, Journal of Physics: Conference Series, 171, 2020, hereinafter “Velmurugan”, Uyanahewa et al., WONGA: The Future of Personal Finance Management– A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking, 2023 4th International Conference for Emerging Technology (INCET) Belgaum, India. May 26-28, 2023, hereinafter “Uyanahewa” and Wang et al., (US 2021/0073735 A1) hereinafter “Wang” as applied to claims 1 and 11 further in view of Russell McGrane (US 2012/0317003 A1) hereinafter “McGrane”.
Claims 3 and 13:
Velmurugan in view of Uyanahewa and Wang is silent with the following limitations. However, McGrane in an analogous art of expenditure management for the purpose of providing the following limitations as shown does::
sending, from the microservice, a response to the mobile application, wherein the response comprises an identifier of the expenditure entry (¶ 0055: “the expense account report 110 may be sent directly to the user 200. The expense account report 110 may be sent to the user 200 in an electronic form such that the expense account report 110 is maintained in a computer readable format. For example, the expense account report 110 may be sent to the user 200 by way of an electronic communication modality such as e-mail or the like (e.g., via the communication network 500). Note that the communication modality used to send the user 200 the expense account report 110 may the same modality used to communicate with the user 200 to collect transaction data or may be a different communication modality.” See also ¶ 0056: “The expense account report 110 may reflect any or all purchase transactions 210 initiated by the user 200 using the transaction card in a predetermined time period.”);
Both Velmurugan and McGrane teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” McGrane teaches in the Abstract: “An automated expense account report generator.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of McGrane would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of McGrane to the teaching of Velmurugan in view of Uyanahewa and Wang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as sending, from the microservice, a response to the mobile application, wherein the response comprises an identifier of the expenditure entry into similar systems. Further, as noted by McGrane “to reduce the administrative burden associated with preparation of expense account reports. Another objective of the present invention is to improve the accuracy of data included in expense account reports. Yet another objective is to provide a user friendly interface that is accessible at the time of or near the time of a purchase transaction.” (McGrane, ¶ 0006).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Velmurugan et al., Expense Manager Application, Journal of Physics: Conference Series, 171, 2020, hereinafter “Velmurugan” in both view of Uyanahewa et al., WONGA: The Future of Personal Finance Management– A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking, 2023 4th International Conference for Emerging Technology (INCET) Belgaum, India. May 26-28, 2023, hereinafter “Uyanahewa”, Wang et al., (US 2021/0073735 A1) hereinafter “Wang” and Russell McGrane (US 2012/0317003 A1) hereinafter “McGrane” , as applied to claims 3 and 13 above, and further in view of Li et al., (US 2019/0235979 A1) hereinafter “Li”.
Claims 4 and 14:
Velmurugan in view of Uyanahewa receive SMS. McGrane teaches receiving and sending text messages with transaction information from the mobile application as shown in Figures 1 and 3-5. Wang teaches microservices as explained above. Velmurugan in view of Uyanahewa, Wang and McGrane is silent with regard to the following limitations. However, Li in an analogous art of transactions management for the purpose of providing the following limitations as shown does:
wherein the microservice switches from a sleep mode to an active mode after receiving the text message from the mobile application and switch back to the sleep mode after sending the response to the mobile application (¶ 0061: “a passive node for those same microservice requests may be lying in a dormant state until switching to an active mode for those specific microservice requests” i.e., receiving/sending. “In some examples, only one node of the entire cluster may be active at a single time for a specific microservice request.”);
Both Velmurugan and Li teach transactions management. Velmurugan a teaches in page 2, 4. Features of Application: “Keep track of all your daily transactions.” Li teaches in ¶ 0038: “microservices may include performing credit card transactions, executing search queries, and/or executing user login and/or authentication procedures.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Li would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Li to the teaching of Velmurugan in view of Uyanahewa, Wang and McGrane in view of would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as the microservice switches from a sleep mode to an active mode [after receiving the text message from the mobile application] and switch back to the sleep mode [after sending the response to the mobile application] into similar systems. Further, as noted by Li “ the term “microservice” generally refers to a modular component within an application that performs a specific task in accordance with a service-oriented software architecture.” (Li ¶ 0038).
Claims 5-8, 15-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Velmurugan et al., Expense Manager Application, Journal of Physics: Conference Series, 171, 2020, hereinafter “Velmurugan” in both view of Uyanahewa et al., WONGA: The Future of Personal Finance Management– A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking, 2023 4th International Conference for Emerging Technology (INCET) Belgaum, India. May 26-28, 2023, hereinafter “Uyanahewa” Wang et al., (US 2021/0073735 A1) hereinafter “Wang”, as applied to claims 1 and 11 above, and further in view of Caric et al., (WO 2009/156773 A1) published on December 30, 2009, hereinafter “Caric”.
Claims 5 and 15:
Velmurugan teaches in page 1: “These analysis patterns can be derived using some data mining techniques such as clustering, classification and association.” Uyanahewa teaches in page 2, “the Named Entity Recognition (NER) model was used to identify key expense details from the extracted text […] Expense data classification and analysis is a major area in financial management. In this system, expenses have been proposed to manage in an automated environment to classify them correctly into correct expense categories using Naïve Bayes text classifications.” . Wang teaches in ¶ 0103: “The expense and/or activity data may be extracted (pushed or pulled) from one or more external sources” Velmurugan in view of Uyanahewa and Wang is silent with regard to the following limitations. However, Caric in an analogous art of data analysis for the purpose of providing the following limitations as shown does:
wherein the extracting comprises identifying words in the text message corresponding to predefined entity classes (page 7, lines 11-14: “Neural networks text analysis module M3 is composed of the following sub modules: Input sub module M301, sub module for removal of special characters M302, sub module for removal of language specific characters M303, sub module for word extraction M304, sub module for number handling M305” and page 13, lines 12-13: “Named Entities correspond to class 2 phrases used by module M3. Other recognized entities having assigned attribute values are treated as class 1 phrases used by module M3.”);
Both Velmurugan and Caric teach data analysis. Velmurugan teaches in page 1: “These analysis patterns can be derived using some data mining techniques such as clustering, classification and association.” Caric teaches in page 6, lines 8-9: “recognizing words or phrases and their meaning from digital free text content.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Caric would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Caric to the teaching of Velmurugan in view of Uyanahewa and Wang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the extracting comprises identifying words in the text message corresponding to predefined entity classes into similar systems. Further, as noted by Caric “automatic recognition of single words or group of words and their meaning from any electronic content source in a form of free text” (Caric, page 6, lines 2-3).
Claims 6 and 16:
Uyanahewa teaches in page 3: “The dataset was manually built with relevant labels to train the model. “ Velmurugan in view of Uyanahewa is silent with regard to the following limitations. However, Wang in an analogous art of expenditure management for the purpose of providing the following limitations as shown does:
and a label for at least one entity class (¶ 0040: “ The output may correspond to a prediction based on prior machine learning. In some embodiments, the output includes a label, classification, and/or categorization assigned to the provided input(s). The machine learning model 126 corresponds to a learned model for performing the desired operation(s) (e.g., labeling, classifying, and/or categorizing inputs).”);
Both Velmurugan and Wang teach expenditure management. Velmurugan teaches in the Abstract: “we develop a mobile application developed for the android platform that keeps record of user personal expenses, his/her contribution in group expenditures, top investment options, view of the current stock market, read authenticated financial news and grab the best ongoing offers in the market in popular categories.” Wang teaches in the Abstract “generating an expense report.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Wang would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Wang to the teaching of Velmurugan in view of Uyanahewa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as label for at least one entity class into similar systems. Further, as noted by Wang “Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications.” “the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output.” (Wang, ¶ 0146 and 0154).
Velmurugan teaches in page 5 Expense Tracker – SMS Reading. Uyanahewa teaches in page 3: “Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns. For extracting the amount and transaction type, two patterns were employed, and the information was extracted accordingly.” Wang teaches in ¶ 0103: “The expense and/or activity data may be extracted (pushed or pulled) from one or more external sources” Velmurugan in view of Uyanahewa and Wang is silent with regard to the following limitations. However, Caric in an analogous art of data analysis for the purpose of providing the following limitations as shown does:
further comprising defining a character sequence pattern […] for at least one entity class (page 7, lines 11-14: “After the extraction of numbers, words arrive in sequence to sub module M306 that determines to which word class words belong to and, based on this, forwards them to the appropriate net for word normalization,”);
Both Velmurugan and Caric teach data analysis. Velmurugan teaches in page 5 a Sms Reading Caric teaches in page 6, lines 8-9: “recognizing words or phrases and their meaning from digital free text content.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Caric would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Caric to the teaching of Velmurugan in view of Uyanahewa and Wang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as defining a character sequence pattern for at least one entity class into similar systems. Further, as noted by Caric “automatic recognition of single words or group of words and their meaning from any electronic content source in a form of free text” (Caric, page 6, lines 2-3).
Claim 7:
Velmurugan teaches in page 5 Expense Tracker – SMS Reading. Uyanahewa teaches in page 3: “Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns. For extracting the amount and transaction type, two patterns were employed, and the information was extracted accordingly.” Wang teaches in ¶ 0103: “The expense and/or activity data may be extracted (pushed or pulled) from one or more external sources” Velmurugan in view of Uyanahewa and Wang is silent with regard to the following limitations. However, Caric in an analogous art of data analysis for the purpose of providing the following limitations as shown does:
wherein the character sequence pattern for the at least one entity class represents multiple consecutive decimal numbers (page 13, lines 26-34 to page 14, line 1: “This phase encompasses the sub module for number handling M305 which is responsible for the extraction of numbers contained within the words, if any. The first and second phase product is a sentence that consists of normalized words and previously extracted numbers arranged in order corresponding to the original text. Each extracted word arrives to sub module M306 which selects the subnet responsible for the normalization of particular word class. Depending on its class, a word at the entry point of sub module M306 is forwarded to the appropriate subnet - the normalizer - consisting of sub modules M307 to M331. So, for each word class there is a corresponding normalizing module trained according to the initial learning word set of the appropriate class.”);
Both Velmurugan and Caric teach data analysis. Velmurugan teaches in page 1: “These analysis patterns can be derived using some data mining techniques such as clustering, classification and association.” Caric teaches in page 6, lines 8-9: “recognizing words or phrases and their meaning from digital free text content.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Caric would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Caric to the teaching of Velmurugan in view of Uyanahewa and Wang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the character sequence pattern for the at least one entity class represents multiple consecutive decimal numbers into similar systems. Further, as noted by Caric “automatic recognition of single words or group of words and their meaning from any electronic content source in a form of free text” (Caric, page 6, lines 2-3).
Claims 8 and 17:
Velmurugan teaches in page 5 Expense Tracker – SMS Reading. Uyanahewa teaches in page 3: “Once the model identified the SMS as a transaction type, key information like the date and amount was extracted from the SMS using two patterns. For extracting the amount and transaction type, two patterns were employed, and the information was extracted accordingly.” Velmurugan in view of Uyanahewa and Wang is silent with regard to the following limitations. However, Caric in an analogous art of data analysis for the purpose of providing the following limitations as shown does:
wherein the evaluating comprises comparing the plurality of named entities with pre-registered entity data (page 12, lines 28-31: “Upon extraction of Named Entities from preprocessed text follows the fourth phase during which the meaning of particular words contained in a preprocessed text is determined by the use of sub module M25 functionality. Sub module M25 contains a dictionary sub module M251 for each language defined in device application domain.” And page 15, lines 13-15 : “the objects are matched by the identifier, which is to the recognized and normalized word, for which sub module M405 is responsible. After that, sub module M406 matches the values of recognized and previously compared objects.”);
Both Velmurugan and Caric teach data analysis. Velmurugan teaches in page 1: “These analysis patterns can be derived using some data mining techniques such as clustering, classification and association.” Caric teaches in page 6, lines 8-9: “recognizing words or phrases and their meaning from digital free text content.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Caric would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Caric to the teaching of Velmurugan in view of Uyanahewa and Wang would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein the evaluating comprises comparing the plurality of named entities with pre-registered entity data into similar systems. Further, as noted by Caric “automatic recognition of single words or group of words and their meaning from any electronic content source in a form of free text” (Caric, page 6, lines 2-3).
Claim 20:
The limitations of claim 20 (Velmurugan, Abstract: we develop a mobile application developed for the android platform) encompasses substantially the same scope as claims 1, 3 and 5. Accordingly, those similar limitations are rejected in substantially the same manner as claims 1, 3 and 5, as described above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
S. Ye, D. -a. Liao and Y. Zhou, "Transaction Dialogs Based on Short Messages Understanding," 2011 Fourth International Symposium on Knowledge Acquisition and Modeling, Sanya, China, 2011, pp. 117-120. Describe “[a]fter determining the domain of a new dialog using SPN, the service agent SMartS extracts information from SMs that are parsed by SM parser, part-of-speech tagger, name entities recognizer and semantic parser in turn, and then replies the prompt which could direct the procedure of dialog. An induction algorithm is used to discover automatically most domain-specific parser rules from the corpus which contains original SMs and the predefined case frame which depicts detailed semantic groups.
S. A. Sabab, S. S. Islam, M. J. Rana and M. Hossain, "eExpense: A Smart Approach to Track Everyday Expense," 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh, 2018, pp. 136-141. Describe “also monitors user’s income by tracking the received SMS’s from the user’s saving accounts. By calculating income and expense it produces the user’s balance in monthly and yearly basis. Overall, this is a smart automated solution for tracking expense.”
S. Vatsal, N. Purre, S. Moharana, G. Ramena and D. Mohanty, "On-Device Information Extraction from SMS using Hybrid Hierarchical Classification," 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 2020, pp. 178-181, Describe “a unique architecture to organize and extract the appropriate information from SMS and further display it in an intuitive template”.
A. D. Prabhu, N. Arora, S. Vatsal, G. Ramena, S. Moharana and N. Purre, "On-Device Sentence Similarity for SMS Dataset," 2021 IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2021, pp. 140-146, Describe “pipeline deals with major semantic variations across SMS data as well as makes it effective for its application on-device (mobile phone)”
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 NADJA CHONG whose telephone number is (571)270-3939. The examiner can normally be reached on Monday-Friday 8:00 am - 2:00 pm ET, Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, RUTAO WU can be reached on 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
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