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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-8 are directed to a process, claims 9-16 are directed to a manufacture, and claims 17-20 are directed to a machine. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“obtaining an image of a receipt, wherein the receipt includes a list of item identifiers and associated charges for an order; identifying a retailer of the associated receipt; providing a prompt to a first machine-learning model including the image of the receipt or extracted information from the image of the receipt, and a request to provide a set of item descriptors corresponding to the list of item identifiers in the image; receiving, from the first machine-learning model as a response, the set of item descriptors, wherein an item descriptor in the set is a description of a respective item in the order in the receipt, wherein the item descriptor is different from the corresponding item identifier of the respective item; mapping the set of item descriptors to one or more items in a catalog database associated with the retailer; generating an online order including the one or more mapped items; and transmitting instructions … to cause display of an ordering interface with the online order. Claim 1.
With recited additional elements reserved for consideration alone and all together combined with their recited role(s) in the claim under step 2A prong two, a careful analysis of the remaining limitations above results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract idea falls within the groupings of abstract ideas described as mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and certain methods of organizing human activity, for example commercial interactions (including advertising, marketing or sales activities or behaviors). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional element in the present claims is a client device. This additional element has been considered individually and in combination with the functions it performs, e.g., the client device is the recipient of transmitted instructions to cause it to display an ordering interface with the online order. This does not integrate the judicial exception into a practical application because it amount to no more than an implication that the exception might be applied using other unidentified generic computer components. The claim is almost entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of abstract steps. There are no identified functional operations performed by any identified device or structural element to perform the steps or otherwise obtain the intended results. The additional element does not improve the functioning of any computer or other technology or technical field, it does not apply the judicial exception with or by use of a particular machine, it does not transform or reduce a particular article to a different state or thing, and it fails to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional element has not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional element has not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the lone additional element of the claim is a mere prop implying that the abstract idea or other exception is in a computer environment. MPEP 2106.05(f). The claims invoke devices merely as tangential tools to perform an abstract process. Simply adding general purpose computer components to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The claims fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that dependent claims 2-3, 6-7, 10-11, 14-15, and 18-19 do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the data and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one."). Dependent claims 5, 8, 13, and 16, add two additional token client devices, “a client device associated with an auditor” in claims 5 and 13 and “a picker client device” in claims 8 and 16. Similar to the client device of the independent claims these devices perform no identified operations, merely being recipients of information, essentially as stand ins for human users not recited in the process. Claims 4, 12, and 20, further utilize the original client device as a source for feedback from a user. There is still no indication that it performs any particular function or operation.
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 112
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 2, 10, and 18, 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims recite “providing the prompt to the first machine-learning model comprises providing the image of the receipt to a multi-modal transformer architecture,” but depend from claims 1, 9, and 17, respectively, and claims 1, 9, and 17, recite “providing a prompt to a first machine-learning model including the image of the receipt or extracted information from the image of the receipt.” Providing the image of the receipt is therefore an element that claim recitations 1, 9, and 17, explicitly do not require, but that is required by the recitations of claims 2, 10, and 18. The claims are therefore indefinite.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-14, and 16-20, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Durazo Almeida et al. (Patent No.: US 11,257,049 B1).
Durazo Almeida teaches all the limitations of claims 1-6, 8-14, and 16-20. For example it discloses obtaining an image of a receipt and using a machine learning model to extract information and determine item descriptors therefrom to map to items in a product catalog. Durazo Almeida further discloses pertaining to
Claim 1. A method, comprising: ● obtaining an image of a receipt, wherein the receipt includes a list of item identifiers and associated charges for an order (see at least Durazo Almeida abstract “An image of a receipt may be received by a service provider from a user device of a user. The receipt may be a record of a transaction between the user and a physical merchant,” fig.3); ● identifying a retailer of the associated receipt (see at least Durazo Almeida abstract “receipt may be a record of a transaction between the user and a physical merchant,” figs.3, 6); ● providing a prompt to a first machine-learning model including the image of the receipt or extracted information from the image of the receipt, and a request to provide a set of item descriptors corresponding to the list of item identifiers in the image (see at least Durazo Almeida abstract “The service provider may extract text from the image and apply a predictive model trained on text data of historical receipts to the extracted text to identify keywords corresponding to items purchased in the transaction,” figs. 1A, 5A. Please note: the claim language consisting of a series of optional or alternative limitations separated by “or” does not result in further limitation beyond a single alternative because beyond the presence of any single alternative it merely represents contingencies that are not required. Applicant is reminded that optional or conditional elements do not narrow the claims because they can always be omitted. See e.g. MPEP §2111.04 "Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure."; and In re Johnston, 435 F.3d 1381,77 USPQ2d 1788, 1790 (Fed. Cir. 2006) ("As a matter of linguistic precision, optional elements do not narrow the claim because they can always be omitted.")); ● receiving, from the first machine-learning model as a response, the set of item descriptors, wherein an item descriptor in the set is a description of a respective item in the order in the receipt, wherein the item descriptor is different from the corresponding item identifier of the respective item (see at least Durazo Almeida abstract “identify keywords corresponding to items purchased in the transaction,” figs. 1A, 3, 5A, 6A, c1:65-c2:5 “service provider may ingest images of hard copy receipts showing items ("receipt items") purchased from a physical store and generate a list of keywords that the service provider predicts are words that describe the receipt items”); ● mapping the set of item descriptors to one or more items in a catalog database associated with the retailer (see at least Durazo Almeida abstract “service provider may … compare the keywords to descriptions of catalog items in an online catalog maintained by the service provider to identify catalog items that are the same or similar to the purchased items,” fig.2, c2:45-50 “"Mapping," as used herein, may include making associations between data within a database or data structure. Thus, when a receipt item is "mapped" to a keyword or catalog item, data representing the receipt item is associated with data representing one or more keywords or catalog items”); ● generating an online order including the one or more mapped items (see at least Durazo Almeida c2:45-54 “when a receipt item is "mapped" to a keyword or catalog item, …. The service provider can … use the keywords or catalog items to populate a user's virtual cart or a user's virtual shopping list,” c4:3-15 “keywords for items of the list of items may be mapped to online catalog items …. One or more of the catalog items can be presented via a graphical user interface to the user (e.g., in a virtual cart of the user, a virtual shopping list of the user, etc.)”); and ● transmitting instructions to a client device to cause display of an ordering interface with the online order (see at least Durazo Almeida abstract “service provider may … generate a user interface to present on the user device that includes an indication of the catalog items,” figs. 1B, 4, 7, c4:3-15 “One or more of the catalog items can be presented via a graphical user interface to the user (e.g., in a virtual cart of the user, a virtual shopping list of the user, etc.)”).Claim 2. The method of claim 1, wherein providing the prompt to the first machine-learning model comprises providing the image of the receipt to a multi-modal transformer architecture (see at least Durazo Almeida abstract “image of a receipt may be received by a service provider from a user device of a user. … The service provider may extract text from the image and apply a predictive model trained on text data of historical receipts to the extracted text to identify keywords corresponding to items purchased in the transaction”).Claim 3. The method of claim 1, wherein the method further comprises: ● extracting text data from the image using optical character recognition (OCR), wherein the text data includes the list of item identifiers, and wherein the prompt to the first machine-learning model includes the text data (see at least Durazo Almeida c6:10-20 “service provider uses an optical character recognition (OCR) algorithm or other text recognition method to determine the words and/or characters”).Claim 4. The method of claim 1, wherein the method further comprises: ● collecting feedback from the client device, wherein the feedback indicates that a user of the client device converted on the online order (see at least Durazo Almeida fig.7, c4:50-60 “service provider may receive direct or indirect feedback from a user about the fit of catalog items that the service provider suggested to purchased items (e.g., via user comments, items ordered by users, etc.).”); ● generating a training example for training the first machine-learning model, wherein the training example includes the prompt and the set of item descriptors (see at least Durazo Almeida c4:50-65 “keyword can be associated (by query, mapping, etc.) with one or more catalog items (e.g., one or more item identifiers) via the data model. The service provider may receive direct or indirect feedback from a user about the fit of catalog items that the service provider suggested to purchased items (e.g., via user comments, items ordered by users, etc.). Such feedback may be used as training data in an item identifier data model. For example, that a user purchased a particular catalog item recommended by the service provider based on a keyword may be used as training data for linking the keyword to the item identifier.”); and ● training parameters of the first machine-learning model using the training example (see at least Durazo Almeida c4:50-65 (as above)).Claim 5. The method of claim 1, further comprising: ● receiving, from a user, a request to fulfill a second online order, the second online order including one or more items (see at least Durazo Almeida c2:45-55 “receipt item is "mapped" to a keyword or catalog item, data representing the receipt item is associated with data representing one or more … catalog items. The service provider can provide a list of … catalog items to populate a user's virtual cart or a user's virtual shopping list maintained by the service provider,” c8:40-65 “designates a catalog item as a catalog item to include in a saved or recurring shopping list (e.g., a base list for weekly grocery shopping, a base list for a recipe, a base list for a holiday meal, etc.), etc.” Please note: it is examiner’s position that the reference describes a method that would be understood by a person of ordinary skill in the art to be practiced repetitively. A person of ordinary skill in the technical environment described would assume that this is true unless a particular restriction otherwise is described. It is also true that since the origin of the receipt must be a first order, the resulting product order list provided therefrom as discussed above, is a second order.); ● obtaining a second image of a second receipt including a second list of item identifiers (see at least Durazo Almeida abstract “extract text from the image and apply a predictive model trained on text data of historical receipts,” c1:55-60 “extracting text from images of receipts showing purchases,” c2:10-15 “The extracted text from the images of the receipts” . Please note: see previous comment.); ● generating a second set of item descriptors corresponding to the second list of item identifiers (see at least Durazo Almeida figs. 1A, 3, 5A, 6A, c1:65-c2:5 “service provider may ingest images of hard copy receipts showing items ("receipt items") purchased from a physical store and generate a list of keywords that the service provider predicts are words that describe the receipt items”. Please note: see previous comment.); ● identifying one or more anomalies in the second receipt by comparing the second list of item descriptors to the one or more items of the second online order (see at least Durazo Almeida c20:20-30 “data regarding the price of the receipt item and the catalog item can be compared to check if the mapping is likely correct or incorrect. For example, given a receipt item with a price of $2.50, a mapped catalog item with a price of$50.00 could likely be an incorrect match”); and ● responsive to identifying the one or more anomalies, providing the second online order to a client device associated with an auditor (see at least Durazo Almeida c4:35-45 “a price associated with a description of an item can indicate that a certain quantity of that item was purchased or confirm what that item is. … that information can be used by the data model in future applications to increase confidence that a keyword is correct,” c13:15-21 “input data can also include descriptive data indicative of attributes of historical receipts, indications of whether any catalog item has been confirmed … service provider verification of keywords correctly reflecting receipt items, etc”).Claim 6. The method of claim 5, wherein identifying the one or more anomalies further comprises: ● providing a second prompt to a second machine-learning model or the first machine-learning model, the second prompt requesting to identify whether there is a difference between the one or more items of the second online order and the second list of item descriptors (see at least Durazo Almeida “apply a predictive model trained on text data of historical receipts to the extracted text to identify keywords corresponding to items purchased in the transaction. The service provider may generate a shopping list for the user and/or may compare the keywords to descriptions of catalog items in an online catalog maintained by the service provider to identify catalog items that are the same or similar”. Please note: see previous comments regarding the presumption that the process is repeated); and ● identifying the one or more anomalies based on a response to the second prompt (see at least Durazo Almeida c20:20-30 “data regarding the price of the receipt item and the catalog item can be compared to check if the mapping is likely correct or incorrect. For example, given a receipt item with a price of $2.50, a mapped catalog item with a price of $50.00 could likely be an incorrect match”).Claim 8. The method of claim 1, further comprises: ● obtaining a second image of a second receipt, wherein the second receipt includes a second list of item identifiers and associated charges for a second order (see at least Durazo Almeida abstract “An image of a receipt may be received by a service provider from a user device of a user. The receipt may be a record of a transaction between the user and a physical merchant,” fig.3. Please see previous comment concerning repeated iterations of the method.); ● generating a second online order including one or more items that correspond to items in the second receipt by at least prompting the first machine-learning model (see at least Durazo Almeida c2:45-54 “when a receipt item is "mapped" to a keyword or catalog item, …. The service provider can … use the keywords or catalog items to populate a user's virtual cart or a user's virtual shopping list,” c4:3-15 “keywords for items of the list of items may be mapped to online catalog items …. One or more of the catalog items can be presented via a graphical user interface to the user (e.g., in a virtual cart of the user, a virtual shopping list of the user, etc.)”. Please see previous comment concerning repeated iterations of the method.); and ● providing the second online order to a picker client device (see at least Durazo Almeida abstract “service provider may … generate a user interface to present on the user device that includes an indication of the catalog items,” figs. 1B, 4, 7, c4:3-15 “One or more of the catalog items can be presented via a graphical user interface to the user (e.g., in a virtual cart of the user, a virtual shopping list of the user, etc.)”).
Pertaining to computer-readable medium claims 9-14 and 16, and system claims 17-20
Rejection of claims 9-14 and 16-20 is based on the same rationale noted above. In addition Durazo Almeida teaches, regarding
Claim 9. A non-transitory computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform operations (see at least Durazo Almeida fig.2).Claim 17. A computer system comprising: ● a computer processor (see at least Durazo Almeida fig.2); and ● a non-transitory computer readable medium storing instructions that, when executed by the computer processor, cause the computer processor to perform the operations (see at least Durazo Almeida fig.2).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Durazo Almeida et al. (Patent No.: US 11,257,049 B1) in view of Chen et al. (Pub. No.: US 2023/0044152 A1) .
Durazo Almeida teaches all of the above as noted. It discloses a) obtaining image and text data to determine item descriptors , b) mapping descriptors to items in a product catalog, c) recommending products, and d) the catalog associated with a retailer, but does not explicitly disclose using an embedding model to generate an embedding for the item descriptor, comparing the embeddings of the item descriptors to embeddings representing items in the catalog database, and mapping the item descriptors to the one or more items in the catalog database based on a similarity between the embeddings of the item descriptors.
Chen also teaches a) obtaining image and text data to determine item descriptors , b) mapping descriptors to items in a product catalog, c) recommending products, and d) the catalog associated with a retailer, and further discloses, regarding
Claim 7. The method of claim 1, wherein mapping the set of item descriptors to one or more items in a catalog database associated with the retailer further comprises: ● processing each item descriptor from the set of item descriptors using an embedding model to generate an embedding for the item descriptor (see at least Chen figs.2, 4, ¶0018 “model is trained and encodes a textual product title into an embedded sequence”); ● comparing the embeddings of the item descriptors to embeddings representing items in the catalog database (see at least Chen ¶0018 “model is trained and encodes a textual product title into an embedded sequence,” ¶0026 “Storage of data includes a database of searchable products stored (with other data) in Data Lake 24 with digitally stored details and descriptors on products and virtual storefronts searchable by Users”); and ● mapping the item descriptors to the one or more items in the catalog database based on a similarity between the embeddings of the item descriptors (see at least Chen ¶0004 “to map these products to their locations in a product category taxonomy tree efficiently and accurately so that buyers can easily find the products they need”).
Therefore it would have been obvious to one of ordinary skill in the art at the time of invention (for pre-AIA applications) or filing (for applications filed under the AIA ) to modify the method of Durazo Almeida to include using an embedding model to generate an embedding for the item descriptor, comparing the embeddings of the item descriptors to embeddings representing items in the catalog database, and mapping the item descriptors to the one or more items in the catalog database based on a similarity between the embeddings of the item descriptors, as taught by Chen since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately. One of ordinary skill in the art would have recognized that the results of the combination were predictable and would result in an improvement. This is because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such features even from a variety of technical fields into methods and systems implemented using similar technological structures (i.e., generic computer and/or network hardware such as processors, servers, etc.). In this case the areas of technical endeavor are nonetheless similar and overlapping.
Applicant has not disclosed that the added feature solves any stated problem or is for any particular purpose beyond the performance of the functions they performed separately and since each element and its function are shown in the prior art the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would therefore have been an obvious matter of design choice to include the feature from Chen in the method of Durazo Almeida. Furthermore the combination solved no long felt need. Incorporating cumulative known features is additionally obvious to one of ordinary skill in the art because doing so increases commercial use of a method by attracting users that previously might have chosen between one of the previously known methods.
Pertaining to computer-readable medium claim 15
Rejection of claim 15 is based on the same rationale noted above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● Trandal et al., Patent No.: US 8,285,604 B1: teaches receipt management and shopping list creation.
● Brelig et al., Pub. No.: US 2017/0206536 A1: teaches collecting image data of a purchase receipt, and generating a receipt model including at least one line item; selecting a product profile to associate with the line item through a tiered set of receipt heuristics and calculating a score for the line item compared to at least one product profile of a master product list.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 July 28, 2026
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