DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Action is in response to the Amendment filed June 20, 2024. Claims 1, 3-4 and 6 are amended. Claims 7-11 are cancelled. Claims 1-6 are pending and have been examined in this application.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In Claim 1 the limitation “clustering the detailed coupon information corresponding to each of the detected coupons based on one or more predetermined criteria including an original coupon value of the detected coupons, and storing the clustered result in the coupon database or the local memory,” is not described in the original disclosure. The specification states that “extracted information will be grouped and sorted according to a predetermined criteria so that, whenever the user wants to manage coupons, those extracted detailed coupon information can be instantly displayed to the consumer.” [0142]. This does not describe that detailed coupon information is clustered based on any predetermined criteria that includes an original coupon value of the detected coupons. This is directed to impermissible new matter. Claims 2-6 by being dependents of claim 1 are also rejected.
In Claim 1 the limitation “calculating a derivative coupon value based on the original coupon value and creating at least one additional clustering criterion to said predetermined criteria and incorporating the derivative coupon value to the detailed coupon information,” is not described in the original disclosure. The specification states that the “the detailed coupon information can be not only the information written on the coupon itself (for example, such as discount rate of a coupon), but also some derivative information such as coupon-cash exchange value,” [0068], and the “the detailed coupon information includes at least one of coupon name, customer benefit, valid period, eligible use category, eligible seller list, or equivalent value calculated by a predetermined criteria for each of the detected coupons,” [0150]. This does not describe that 1) a derivative coupon value is calculated based on the original coupon value, 2) that any additional clustering criterion is created according to said predetermined criteria, or 3) that the calculated derivative coupon value based on the original coupon value is incorporated into the detailed coupon information (that includes an original coupon value). The claim draftsman has conflated two separate notions. Therefore, applicant fails to have support for this limitation. Accordingly, the claims are directed to impermissible new matter. Claims 2-6 by being dependents of claim 1 are also rejected.
The following is a quotation of 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 1-6 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.
Claim 1 recite the limitation “the clustered result” in lines 14 and 18. There is insufficient antecedent basis for the limitation in the claim. Claims 2-6 by being dependents of claim 1 are also rejected.
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 1-6 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.
Claims 1-6 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-7 are directed toward at least abstract idea without significantly more. In accordance with MPEP § 2106, the rationale for this determination is explained below.
Representative claim 1 is directed towards a method, which is a statutory category of invention.
Although, claim 1 is directed toward a statutory category of invention, the claim, however is directed toward a judicial exception namely an abstract idea. The limitations that set forth the abstract idea recite:
extracting raw data stored as text, image, video, or any combination thereof in the IT device by an artificial intelligence (AI) tool of the IT device or a coupon Al server;
performing a second analysis on the detected coupons to extract a detailed coupon information from each of the detected coupons;
clustering the detailed coupon information corresponding to each of the detected coupons based on one or more predetermined criteria including an original coupon value of the detected coupons, and storing the clustered result;
calculating a derivative coupon value based on the original coupon value and creating at least one additional clustering criterion to said predetermined criteria and incorporating the derivative coupon value to the detailed coupon information;
and displaying the detailed coupon information selected from the clustered result according to a user request for coupon management. These limitations, comprise commercial interactions including, advertising, marketing or sales activities and business relations, as well as managing personal behavior including following rules or instructions. As such, the limitations are directed towards the abstract grouping of Certain Methods of Organizing Human Activity in prong one of step 2A of the Alice/Mayo test (see MPEP 2106.04(a)(2) II).
This judicial exception is not integrated into a practical application because, when analyzed as a whole under prong two of step 2A of the Alice/Mayo test (see MPEP 2106.04(d)), the additional elements provided by the claim amount to insignificant extra-solution activity and mere use of a computer as a tool to perform or apply the abstract idea. In particular the claim recites the additional element of: extracting raw data stored as text, image, video, or any combination thereof in the IT device by an artificial intelligence (AI) tool of the IT device or a coupon Al server; performing a first analysis on the raw data by the Al tool or the coupon Al server to automatically detect one or more coupons from the raw data, and storing the detected coupons in a coupon database of the coupon Al server or a local memory, which amounts to obtaining a particular data source or type of data to be manipulated and data gathering necessary to implement the judicial exception. See MPEP 2106.05(g). While, the limitations, in the coupon database or the local memory; in the coupon database or the local memory;
on the IT device, through the coupon database or the local memory, are recited at a high level of generality and are merely the use of a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Simply adding insignificant extra-solution activities and merely applying the abstract idea by a computer. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional elements that does so, or amount to significantly more than the abstract idea. Viewing the limitations individually, extracting raw data stored as text, image, video, or any combination thereof in the IT device by an artificial intelligence (AI) tool of the IT device or a coupon Al server; and the performing a first analysis on the raw data by the Al tool or the coupon Al server to automatically detect one or more coupons from the raw data, and storing the detected coupons in a coupon database of the coupon Al server or a local memory are used only for insignificant extra-solution activity because such activities are used for providing the data source or type of data, and necessary data gathering used to implement the aforementioned abstract idea, see MPEP 2106.05(g). The courts have recognized performing repetitive calculations; receiving, processing, and storing data; electronic scanning or extracting data, and receiving or transmitting data over a network to be well‐understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity. See MPEP 2106.05(d)II; Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362 (Fed. Cir. 2016); Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015). Moreover, it is well known and conventional to extract/analyze/use raw date in the form of text, image, video, or any combination thereof by an artificial intelligence tool. See a least Hiscox et al. (US 20250086705 A1); Balasubramaniam et al. (US 20210097567 A1); Figueroa et al. (US 20240177113 A1); Yadav et al. (US 20220309429 A1) Ramirez (US 20230162245 A1) Figueroa et al. (US 20230186328 A1); Park (US 20110225031 A1); Snopek et al. (US 20230281654 A1). The additional limitations of, AI tool, IT device, AI server, a coupon database, local memory, also do not constitute significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment1. Merely applying an exception using generic computer components cannot provide an inventive concept. Therefore, the limitations of the claims as a whole, when viewed individually and as an ordered combination, do not amount to significantly more than the abstract idea.
A review of dependent claims 2-6, likewise, do not recite any limitations that would remedy the deficiencies outlined above. The claims only further add to the abstract idea, with no elements which integrate the abstract idea into a practical application or constitute significantly more. For instance, claim 2, uses AI as a computer tool to further apply the abstract idea. Thus, while the dependent claims may slightly narrow the abstract idea by further describing it, they do not make it less abstract and are rejected accordingly.
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 of this title, 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 are rejected under 35 U.S.C. 103 as being unpatentable over Balasubramaniam (US Publication 2021/0097567) in view of Chunduri (US Publication 2023/0177553) in further view of Smith (US Publication 2019/0272557).
A. In regards to Claim 1, Balasubramaniam teaches method and system for managing multiple coupons by an information technology (IT) device, comprising:
extracting raw data stored as text, image, video, or any combination thereof in the IT device by an artificial intelligence (AI) tool of the IT device or a coupon Al server; Balasubramaniam [0080: received signal such as a text message, an e-mail message, web page download, or other data may be processed by the communication subsystem and input to the processor; 0047: coupon offering server may implement one or more machine learning techniques for processing received image data; 0100: receipt processor processes the receipt image data using one or more machine learning techniques];
performing a first analysis on the raw data by the Al tool or the coupon Al server to automatically detect one or more coupons from the raw data, and storing the detected coupons in a coupon database of the coupon Al server or a local memory; Balasubramaniam [0044: server platform processes the received photos, retrieves matching coupons based on analysis of image data from the photos and source coupon data; 0047: system may utilize different machine learning techniques for processing different types of received image data; 0212: camera accessor module is configured to access a camera of the user device in order to acquire and/or access image data that can be analyzed by the system; 0151: fast RCNN may help the coupon offering system achieve near real-time detection; 0200: the coupons may be stored in the coupon database such that the coupons can be searched and matched using the detected item data];
performing a second analysis on the detected coupons stored in the coupon database or the local memory to extract a detailed coupon information from each of the detected coupons; Balasubramaniam [0279: item description from the coupon may be the same or similar to the item description retrieved from the database such that a search of a coupon database using the item description includes matching the item description in the search to an item description for the coupon; 0305: image processor may implement a machine learning model trained using an item dataset; the trained model can detect an object and generate detected item data; detected item data is passed by the shopping container image processor to the coupon matcher engine for use in the coupon matching process];
and displaying the detailed coupon information selected from the clustered result according to a user request on the IT device for coupon management through the coupon database or the local memory. Balasubramaniam [0009: receive matched coupon data from the coupon offering server and display the matched coupon data; 0217: the notification module may determine, generate, and display a coupon-related notification; the coupon-related notification provides the user with information about the user's matched coupons];
Balasubramaniam does not specifically disclose, clustering the detailed coupon information corresponding to each of the detected coupons based on one or more predetermined criteria including an original coupon value of the detected coupons, and storing a clustered result in the coupon database or the local memory; this is disclosed by Chunduri [0034: coupon metadata may be used to classify whether the coupon is a sitewide coupon or a non-sitewide coupon. Sitewide coupons are not specific to a brand or category of product and are applicable across the merchant's websites. Non-sitewide coupons are applicable to a certain categories, brands, or products; 0038: bottom up generator uses the product class taxonomy for the merchant to cluster the product classes for which the product name, the product class, or brand information is applicable; as such, the bottom up generator uses the hierarchical structure of the product class taxonomy for the merchant to broaden the coupon metadata to more general products classes; 0040: new coupon text rewrite the original coupon text and provide different combinations of the product classes and brands; 0041: example use case includes a sitewide coupon for “10% off” for a merchant that sells children's toys; .new coupon title generated for the new coupon text is “10% off toy cars; 0052: generated coupon text is added to the coupon catalogs databases and associated with the original coupon; 0048 another example, a coupon text of “$50 off on all products on a merchant's website” for a coupon. A new coupon text is generated that includes “$50 off on a specific brand of dog food;” post processing model keeps the new coupon text based on determining that the new coupon text is applicable to the pricing for the specific brand of dog food];
additionally and/or alternatively Chunduri discloses, performing a second analysis on the detected coupons stored in the coupon database or the local memory to extract a detailed coupon information from each of the detected coupons; Chunduri [0032: coupon metadata extractor obtains coupon data for a plurality of coupons from one or more coupon catalogs (database); 0033: coupon metadata extractor uses one or more machine learning models to extract the coupon metadata from the coupons; 0055: coupon metadata extractor obtains the coupon data within a given cadence and the generative model may automatically generate the new coupon text for the coupon data to upload to the coupon catalogs].
and displaying the detailed coupon information selected from the clustered result according to a user request on the IT device for coupon management through the coupon database or the local memory. Chunduri [0098: browser presenting selected coupon text (e.g., the top ten coupons) for the product class or merchants that the user is currently browsing; as such, the application may publish for every category in the product class, selected coupon text for the user to consume];
it would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Balasubramaniam with the teachings from Chunduri with the motivation to provide a method used for expanding coupon catalogs databases with different variations for a same coupon by providing a plurality of new coupon text with different products, product classes, and/or brands for the same coupon. Chunduri [0110].
Balasubramaniam does not specifically disclose, calculating a derivative coupon value based on the original coupon value and creating at least one additional clustering criterion to said predetermined criteria and incorporating the derivative coupon value to the detailed coupon information. This is disclosed by Smith [0079: price management system can identify an original price of the target product from the product data and then analyze a plurality of possible discount prices based on the original price; the price management system can select the possible discount prices by selecting (incorporating) common price points e.g., historical prices of products in the product category].
It would have been obvious before the effective filing date of the invention for one of ordinary skill in the art to have modified the teachings of Balasubramaniam with the teachings from Smith with the motivation to identify customer data for a plurality of customers for generating discount prices customized to a customer demographic, a particular day part, or for a particular store location, which can include purchasing habits in relation to products of a product category or related product categories, price sensitivity, e.g., an aggregate score of how heavily a customer is influenced by price or brand, whether the customer uses coupons or offers. Smith [0068].
B. In regards to Claim 2, Balasubramaniam discloses, wherein the first analysis or the second analysis is performed by the AI tool comprised of a language analysis tool, image or video analysis tool, or any combination thereof. Balasubramaniam [0144: using Fast CNN, an input image is fed to a convolutional neural network (CNN). The CNN generates convolutional feature maps; Fast CNN uses a single model which extracts features from the regions, divides them into different classes, and returns the boundary boxes for the identified classes simultaneously].
C. In regards to Claim 3, Balasubramaniam does not specifically disclose, further comprising: optimizing the clustered result by removing from the coupon database or local memory one or more erroneous coupons that turned out not to include marketing information which may be translated into the detailed coupon information during the clustering. This is disclosed by Chunduri [0055: as the coupons expire, the coupons and the associated new coupon text are removed from the coupon catalogs to ensure that coupon catalogs maintain current coupons]. The motivation being the same as that set forth in claim 1.
D. In regards to Claim 4, Balasubramaniam does not specifically disclose, wherein the detailed coupon information includes at least one of coupon name, customer benefit, valid period, eligible use category, eligible seller list, or equivalent value calculated by predetermined criteria for each of the detected coupons. This is disclosed by Chunduri [0023: each coupon identifies the merchant for the coupon; each coupon has a coupon title that provides information about the discount and any constraints that are applicable to the coupon; user may use the different sitewide coupons for any products or brands purchased on the corresponding merchant's websites within the constraints provided in the titles of the coupons]. The motivation being the same as that set forth in claim 1.
E. In regards to Claim 5, Balasubramaniam discloses, further comprising: if the detailed coupon information includes the valid period, enabling the IT device to set an alert function to alarm an expiration of the valid period as for each or all of the detected coupons based on the user request. Balasubramaniam [0217: coupon-related notification provides the user with information about the user's matched coupons, such as an upcoming expiry date of a matched coupon].
F. In regards to Claim 6, Balasubramaniam discloses, wherein the data stored as text, image, video, or any combination thereof in the IT device may include information about a sender of a marketing message to the IT device; an information about an app manager who sent an in-app marketing message to the IT device; or image or video files stored in or pending for deletion from the IT device by a camera function or a screen-recording function of the IT device. Balasubramaniam [0212: camera accessor module is configured to access a camera of the user device in order to acquire and/or access image data that can be analyzed by the system].
Response to Arguments
Applicant's filed arguments have been fully considered but have not been found persuasive.
A. Applicant argues regarding the 35 U.S.C. § 101 rejection that Claim 1, as amended, is directed to a computer-implemented method that includes the use of computer devices and processes, which is more than merely an insignificant extra-solution because the claimed invention as a whole utilizes an IT device, which might be a user smartphone including its memory, to perform significant processes including storage, extraction, Al analysis, clustering, mathematical calculation and data display for the purpose of coupon management. The Examiner respectfully disagrees. The claim is directed to an abstract idea grouped under Certain Methods of Organizing Human Activity because they comprise commercial interactions including, advertising, marketing or sales activities and business relations, as well as managing personal behavior including following rules or instructions. The abstract idea is not integrated into a practical application because the claim merely uses computer components to apply the abstract idea. See 101 analysis; See at least, TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (“It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea”).
As such, the claims as a whole, in view of Alice, do not connote an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment. Therefore, the 35 U.S.C. § 101 rejection is maintained.
B. In regards to the 35 U.S.C. § 103 rejection, Applicant argues that Balasubramaniam does not disclose the concept of "raw data" defined in amended claim 1. The Examiner respectfully disagrees. Balasubramaniam discloses the concept of raw data, by receiving saved raw data, in that it receives signal such as a text message, an e-mail message, web page download, (raw data) or other data that is processed by the communication subsystem and input to the processor. Balasubramaniam [0080].
C. Applicant’s other arguments are moot in light of the new grounds of rejection.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Errol CARVALHO whose telephone number is (571)272-9987. The examiner can normally be reached on M-F 9:30-7:00 Alt Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E CARVALHO/Primary Examiner, Art Unit 3622
1 See, Alice Corp. Pty Ltd. v. CLS Bank lnt'l, 134 S. Ct. 2347, 2360 (2014) (noting that none of the hardware recited “offers a meaningful limitation beyond generally linking ‘the use of the [method] to a particular technological environment,’ that is, implementation via computers” (citing Bilski v. Kappos, 561 U.S. 593, 610-11 (2010))).