DETAILED ACTION
This action is in reply to the Amendments filed on 10/24/2025.
Claims 2, 10, and 18 are cancelled.
Claims 1, 3-9, 11-17, and 19-20 are rejected.
Claims 1, 3-9, 11-17, and 19-20 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 10/24/2025, has been entered. Claims 1, 5-7, 9, 13-15, and 17 have been amended.
Claim Objections
The claim objections from the prior Office Action have been withdrawn pursuant Applicant’s amendments.
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 .
Claim Objections
Claims 1, 3-9, 11-17, and 19-20 are objected to because of the following informalities:
-Claims 1, 9, and 17 read “applying a diagonal matrix to to” but should likely read “applying a diagonal matrix to”
Claims 3-8, 11-16, and 19-20 inherit the deficiencies noted in claims 1, 9, and 17, respectively, and are therefore objected to on the same basis.
Appropriate correction is required.
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, 3-9, 11-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 9 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-identify a set of training data each having a set of features describing quantities of textual receipt item labels and corresponding orders having quantities of order item identifiers;
-train [utilize] a computer model to receive a set of textual receipt item labels and associated quantities and predict a corresponding quantity of item identifiers based on the set of training data;
-apply a diagonal matrix to to the computer model to determine predicted order item identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding [data] in which a corresponding textual receipt item label has a quantity of 1 and all other textual receipt item labels within the respective row have quantities of 0;
-receive, from the computer model, predicted quantities for predicted item identifiers corresponding to the textual receipt item labels with quantities of 1 in the diagonal matrix; and
-map, by the computer model, each textual receipt item label that has a quantity of 1 in the diagonal matrix to at least one order item identifier based on the predicted order item identifiers and predicted quantities of the predicted order item identifiers
The above limitations recite the concept of identifying items in an order receipt. The above limitations fall within the “Certain Methods of Organizing Human Activity” groupings of abstract ideas, enumerated in MPEP 2106.04(a).
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations of identify a set of training data each having a set of features describing quantities of textual receipt item labels and corresponding orders having quantities of order item identifiers; train [utilize] a computer model to receive a set of textual receipt item labels and associated quantities and predict a corresponding quantity of item identifiers based on the set of training data; apply a diagonal matrix to to the computer model to determine predicted order item identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding [data] in which a corresponding textual receipt item label has a quantity of 1 and all other textual receipt item labels within the respective row have quantities of 0; receive, from the computer model, predicted quantities for predicted item identifiers corresponding to the textual receipt item labels with quantities of 1 in the diagonal matrix; and map, by the computer model, each textual receipt item label that has a quantity of 1 in the diagonal matrix to at least one order item identifier based on the predicted order item identifiers and predicted quantities of the predicted order item identifiers are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the data is training data, that the model is a computer model, that the model is trained, and that each row corresponds to a unique one-hot encoding, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “training,” “train,” “computer model,” and “one-hot encoding” language, “identify,” “receive,” “apply,” “receive,” and “map” in the context of this claim encompasses advertising, and marketing or sales activities.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
-identify a set of training data each having a set of features describing quantities of textual receipt item labels and corresponding orders having quantities of order item identifiers;
-train a computer model to receive a set of textual receipt item labels and associated quantities and predict a corresponding quantity of item identifiers based on the set of training data;
-apply a diagonal matrix to to the computer model to determine predicted order item identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding in which a corresponding textual receipt item label has a quantity of 1 and all other textual receipt item labels within the respective row have quantities of 0;
-receive, from the computer model, predicted quantities for predicted item identifiers corresponding to the textual receipt item labels with quantities of 1 in the diagonal matrix; and
-map, by the computer model, each textual receipt item label that has a quantity of 1 in the diagonal matrix to at least one order item identifier based on the predicted order item identifiers and predicted quantities of the predicted order item identifiers
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 9 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea) as supported by paragraph [0095] of Applicant’s specification – “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented with a computer program product comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.” Specifically, the additional elements of a computer program product, a non-transitory computer readable storage medium having instructions encoded thereon, a processor, data being training data, ‘training,’ a computer model, and a one-hot encoding are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of identifying data, receiving data, applying data, and mapping data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of claim 9, taken individually or as a whole, the additional elements of claim 9 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Claim 1 is a method reciting similar functions as claim 9. Examiner notes that claim 1 recites the additional elements of data being training data, ‘training,’ a computer model, and a one-hot encoding, however, claim 1 does not qualify as eligible subject matter for similar reasons as claim 9 indicated above.
Claim 17 is a computer program product reciting similar functions as claim 9. Examiner notes that claim 17 recites the additional elements of computer program product, a processor that executes instructions, a non-transitory computer-readable storage medium having instructions executable by the processor, data being training data, ‘training,’ a computer model, and a one-hot encoding , however, claim 17 does not qualify as eligible subject matter for similar reasons as claim 9 indicated above.
Therefore, claims 1, 9, and 17 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 3-8, 11-16, and 19-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 3-8, 11-16, and 19-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Dependent claims 5-8, 13-16, and 20 do not recite any farther additional elements, and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 3-4, 11-12, and 19 recite the additional elements of the computer model, a multi-dimensional regression model, a random forest model, and a linear regression model , but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 3-8, 11-16, and 19-20 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 3-8, 11-16, and 19-20 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 9, and 17, dependent claims 3-8, 11-16, and 19-20 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. identifying items in an order receipt) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
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 (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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5, 9, 13, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Durazo Almeida at al. (US 11,257,049 B1) hereinafter Durazo Almeida, in view of Iscen et al. (US 2025/0131694 A1), newly cited and hereinafter Iscen.
Regarding claim 1, Durazo Almeida discloses a method comprising:
-identifying a set of training data each having a set of features describing quantities of textual receipt item labels and corresponding orders having quantities of order item identifiers (Durazo Almeida, see at least: “In an example, one type of information (e.g., a price) may refine, or increase the confidence for, a keyword. In some embodiments, the data model may identify one or more fields such as merchant identifier, date, address, etc. which can be used in combination with the description (e.g., OrgBanana) in determining keywords corresponding to that receipt item [i.e. receipt item labels]. In examples, text used as training data a [i.e. identifying a set of training data] may be annotated, tagged, or otherwise indicated as being in a certain category, such as address words, price words, quantity words, etc. [i.e. each having a set of features describing quantities of textual receipt item labels and corresponding orders having quantities of order item identifiers] Such annotation may be treated by the data model as one of the features of the extracted text. In some embodiments, extracted text from a current receipt may be annotated or categorized before being transmitted to the data model. In some embodiments, the extracted text may be categorized as part of the analysis performed with the data model” Col. 6 Ln. 52-67 and “Text data from the text extracted from a physical receipt can be used as training data, [i.e. identifying a set of training data] with or without human annotation, to build a data model that will produce a list of keywords corresponding to the receipt items based on multiple features of the text data. In an example, the data model can a description of an item extracted from a receipt image with a merchant identifier extracted from the receipt image. In another example, 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. Training data may also include information provided by a merchant, digital transaction records of a merchant, seasonal changes in price, promotion history, etc.” Col. 4 Ln. 29-41);
-training a computer model to receive a set of textual receipt item labels and associated quantities and predict a corresponding quantity of item identifiers based on the set of training data (Durazo Almeida, see at least: “In an example, one type of information (e.g., a price) may refine, or increase the confidence for, a keyword. In some embodiments, the data model may identify one or more fields such as merchant identifier, date, address, etc. which can be used in combination with the description (e.g., OrgBanana) in determining keywords corresponding to that receipt item [i.e. to receive a set of textual receipt item labels]. In examples, text used as training data a [i.e. training a computer model] may be annotated, tagged, or otherwise indicated as being in a certain category, such as address words, price words, quantity words, etc. [i.e. and associated quantities] Such annotation may be treated by the data model as one of the features of the extracted text. In some embodiments, extracted text from a current receipt may be annotated or categorized before being transmitted to the data model. In some embodiments, the extracted text may be categorized as part of the analysis performed with the data model” Col. 6 Ln. 52-67 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas [i.e. and predict a corresponding quantity of item identifiers based on the set of training data]” Col. 7 Ln. 1-12);
-determine predicted order item identifiers, and textual receipt item label and other textual receipt item labels (Durazo Almeida, see at least: “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” [i.e. textual receipt item label and other textual receipt item labels] is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items [i.e. determine predicted order item identifiers]. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas” Col. 7 Ln. 1-12);
-receiving, from the computer model, predicted quantities for predicted item identifiers corresponding to the textual receipt item labels (Durazo Almeida, see at least: “the data model [i.e. from the computer model] may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items [i.e. applying a receipt item label of the set of receipt item labels to the computer model to determine predicted order item identifiers]. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas [i.e. receiving predicted quantities for predicted item identifiers corresponding to the textual receipt item labels]” Col. 7 Ln. 1-12); and
-mapping, by the computer model, each textual receipt item label with at least one order item identifier based on the predicted order item identifiers and predicted quantities of the predicted order item identifiers (Durazo Almeida, see at least: “The mapping component 220 receives the extracted text from the text analysis component 218 or text extraction component 216 and generates a list of keywords for the purchased items. The mapping component 220 may also identify item identifiers of catalog items that are the same as, or that are similar to, the receipt item [i.e. each textual receipt item label with at least one order item identifier]. In at least one example, the mapping component 220 includes a data model(s) and a training component that can train the data model(s). The mapping component 220 can identify keywords of the extracted text using the data model(s) [i.e. by the computer model], trained with data from a training database 228. In at least one example, the data model(s) can be trained utilizing machine learning mechanisms in which input data (for instance, in the training database 228) includes text extracted from historical receipts” Col. 13 Ln. 1-15 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items [i.e. based on the predicted order item identifiers]. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas [i.e. and predicted quantities of the predicted order item identifiers]” Col. 7 Ln. 1-12).
Durazo Almeida does not explicitly disclose applying a diagonal matrix to to the computer model to determine predicted order item identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding in which a corresponding textual receipt item label has a quantity of 1 and all other textual receipt item labels within the respective row have quantities of 0; predicted item identifiers corresponding to the textual receipt item labels with quantities of 1 in the diagonal matrix; and mapping, by the computer model, each textual receipt item label that has a quantity of 1 in the diagonal matrix to at least one order item identifier.
Iscen, however, teaches classifying image data (i.e. [0007]), including the known technique of applying a diagonal matrix to the computer model to determine predicted identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding in which a corresponding textual label has a quantity of 1 and all other textual labels within the respective row have quantities of 0 (Iscen, see at least: “The first embedding can then be processed with a classification model to generate a first classification [i.e. to the computer model to determine predicted identifiers]. The classification can include an image classification for a first image. In some implementations, the classification can include one or more object classifications for one or more objects in an image. The classification can include a logit, a softmax output (e.g., the softmax layer output of the logit), and/or one-hot outputs (e.g., a binary prediction of whether the input includes the particular class or not) [i.e. each row corresponding to a unique one-hot encoding in which a corresponding textual receipt item label has a quantity of 1 and all other textual receipt item labels within the respective row have quantities of 0]” [0040] and “The label propagation may be defined formally as follows. Let us assume a graph can be created (or given) for a dataset X, and can be represented by an affinity matrix W, where W.sub.ij=similarity (x.sub.i, x.sub.j) can show that label propagation can be computed by minimizing the following objective” [0152] and “where D is the degree matrix (i.e., diagonal matrix [i.e. applying a diagonal matrix] where the (i,i) entry is the sum of i-th row of W) [i.e. wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding], Y is a matrix containing one-hot label vector of each point in the dataset (i.e., Y.sub.ij=1 if y.sub.i=j and 0 otherwise) [i.e. in which a corresponding textual label has a quantity of 1 and all other textual labels within the respective row have quantities of 0], and μ is a regularization parameter” [0152] and “The input may include, for example, one or more of: image data, moving image/video data, motion data, speech data, audio data, an electronic document … The electronic document data may include text data representing words in a natural language [i.e. a corresponding textual label]” [0045]);
the known technique of predicted identifiers corresponding to the textual labels with quantities of 1 in the diagonal matrix (Iscen, see at least: “The first embedding can then be processed with a classification model to generate a first classification. The classification can include an image classification for a first image. In some implementations, the classification can include one or more object classifications for one or more objects in an image. The classification can include a logit, a softmax output (e.g., the softmax layer output of the logit), and/or one-hot outputs (e.g., a binary prediction of whether the input includes the particular class or not) [i.e. predicted identifiers corresponding to the textual labels]” [0040] and “The label propagation may be defined formally as follows. Let us assume a graph can be created (or given) for a dataset X, and can be represented by an affinity matrix W, where W.sub.ij=similarity (x.sub.i, x.sub.j) can show that label propagation can be computed by minimizing the following objective” [0152] and “where D is the degree matrix (i.e., diagonal matrix where the (i,i) entry is the sum of i-th row of W), Y is a matrix containing one-hot label vector of each point in the dataset (i.e., Y.sub.ij=1 if y.sub.i=j and 0 otherwise) [i.e. the textual labels with quantities of 1 in the diagonal matrix], and μ is a regularization parameter” [0152] and “The input may include, for example, one or more of: image data, moving image/video data, motion data, speech data, audio data, an electronic document … The electronic document data may include text data representing words in a natural language [i.e. the textual labels]” [0045]); and
the known technique of mapping, by the computer model, each textual label that has a quantity of 1 in the diagonal matrix to at least one identifier (Iscen, see at least: “The first embedding can then be processed with a classification model to generate a first classification [i.e. mapping, by the computer model]. The classification can include an image classification for a first image. In some implementations, the classification can include one or more object classifications for one or more objects in an image [i.e. mapping each textual label to at least one identifier]. The classification can include a logit, a softmax output (e.g., the softmax layer output of the logit), and/or one-hot outputs (e.g., a binary prediction of whether the input includes the particular class or not) [i.e. each textual label that has a quantity of 1 in the diagonal matrix]” [0040] and “The label propagation may be defined formally as follows. Let us assume a graph can be created (or given) for a dataset X, and can be represented by an affinity matrix W, where W.sub.ij=similarity (x.sub.i, x.sub.j) can show that label propagation can be computed by minimizing the following objective” [0152] and “where D is the degree matrix (i.e., diagonal matrix where the (i,i) entry is the sum of i-th row of W), Y is a matrix containing one-hot label vector of each point in the dataset (i.e., Y.sub.ij=1 if y.sub.i=j and 0 otherwise) [i.e. each textual label that has a quantity of 1 in the diagonal matrix], and μ is a regularization parameter” [0152] and “The input may include, for example, one or more of: image data, moving image/video data, motion data, speech data, audio data, an electronic document … The electronic document data may include text data representing words in a natural language [i.e. the textual labels]” [0045]). These known technique is applicable to the method of Durazo Almeida as they both share characteristics and capabilities, namely, they are directed to classifying image data.
It would have been recognized that applying the known techniques of applying a diagonal matrix to the computer model to determine predicted identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding in which a corresponding textual label has a quantity of 1 and all other textual labels within the respective row have quantities of 0; predicted identifiers corresponding to the textual labels with quantities of 1 in the diagonal matrix; and mapping, by the computer model, each textual label that has a quantity of 1 in the diagonal matrix to at least one identifier, as taught by Iscen, to the teachings of Durazo Almeida 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 references into similar methods. Further, adding the modifications of applying a diagonal matrix to the computer model to determine predicted identifiers, wherein the diagonal matrix includes a set of rows, each row corresponding to a unique one-hot encoding in which a corresponding textual label has a quantity of 1 and all other textual labels within the respective row have quantities of 0; predicted identifiers corresponding to the textual labels with quantities of 1 in the diagonal matrix; and mapping, by the computer model, each textual label that has a quantity of 1 in the diagonal matrix to at least one identifier, as taught by Iscen, into the method of Durazo Almeida would have been recognized by those of ordinary skill in the art as resulting in an improved method that would provide improved learning (Iscen, [0013]).
Regarding claim 5, Durazo Almeida in view of Iscen teaches the method of claim 1. Durazo Almeida further discloses:
-wherein mapping each textual receipt item label comprises mapping the textual receipt item label to a plurality of predicted order item identifiers having a predicted quantity greater than zero (Durazo Almeida, see at least: “The mapping component 220 receives the extracted text from the text analysis component 218 or text extraction component 216 and generates a list of keywords for the purchased items. The mapping component 220 may also identify item identifiers of catalog items that are the same as, or that are similar to [i.e. to a plurality of predicted order item identifiers], the receipt item [i.e. wherein mapping each textual receipt item label comprises mapping the textual receipt item label]. In at least one example, the mapping component 220 includes a data model(s) and a training component that can train the data model(s). The mapping component 220 can identify keywords of the extracted text using the data model(s), trained with data from a training database 228. In at least one example, the data model(s) can be trained utilizing machine learning mechanisms in which input data (for instance, in the training database 228) includes text extracted from historical receipts” Col. 13 Ln. 1-15 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas [i.e. having a predicted quantity greater than zero]” Col. 7 Ln. 1-12).
Claims 9 and 13 recite limitations directed towards a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, executed by a processor (Durazo Almeida, see at least: “ the blocks represent computer-executable instructions stored on one or more computer- readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types” Col. 18 Ln. 59-66). The rest of the limitations recited in claims 9 and 13 are parallel in nature to those addressed above for claims 1 and 5, respectively, and are therefore rejected for those same reasons set forth above in claims 1 and 5, respectively.
Claim 17 recites limitations directed towards computer program product, comprising: a processor that executes instructions; and a non-transitory computer-readable storage medium having instructions executable by the processor (Durazo Almeida, see at least: “ the blocks represent computer-executable instructions stored on one or more computer- readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types” Col. 18 Ln. 59-66). The rest of the limitations recited in claim 17 are parallel in nature to those addressed above for claim 1, and are therefore rejected for those same reasons set forth above in claim 1.
Regarding claim 20, Durazo Almeida in view of Iscen teaches the computer program product of claim 17. Durazo Almeida further discloses:
-wherein the receipt item label is associated with a plurality of order item identifiers having a predicted quantity greater than zero (Durazo Almeida, see at least: “The mapping component 220 receives the extracted text from the text analysis component 218 or text extraction component 216 and generates a list of keywords for the purchased items. The mapping component 220 may also identify item identifiers of catalog items that are the same as, or that are similar to [i.e. is associated with a plurality of order item identifiers], the receipt item [i.e. wherein the receipt item label]. In at least one example, the mapping component 220 includes a data model(s) and a training component that can train the data model(s). The mapping component 220 can identify keywords of the extracted text using the data model(s), trained with data from a training database 228. In at least one example, the data model(s) can be trained utilizing machine learning mechanisms in which input data (for instance, in the training database 228) includes text extracted from historical receipts” Col. 13 Ln. 1-15 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas [i.e. having a predicted quantity greater than zero]” Col. 7 Ln. 1-12).
Claims 3-4, 11-12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Durazo Almeida, in view of Iscen, in further view of Kunwar et al. (US 2024/0354786 A1), hereinafter Kunwar.
Regarding claim 3, Durazo Almeida in view of Iscen teaches the method of claim 1.
Durazo Almeida in view of Iscen does not teach the computer model being a multi-dimensional regression model.
Kunwar, however, teaches predicting transaction patterns (i.e. abstract), including the known technique of the computer model being a multi-dimensional regression model (Kunwar, see at least: “The financial-transaction-pattern-prediction-based ML-model used for predicting the financial transaction payment pattern in the data prediction module uses a regression-based ML-model. In another embodiment, the regression-based ML-model may include one or more of simple linear regression models, multiple linear regression models [i.e. wherein the computer model is a multi-dimensional regression model], polynomial regression models, support vector regression models, decision tree regression models, random forest regression models, and the like” [0015]). This known technique is applicable to the method of Durazo Almeida in view of Iscen as they both share characteristics and capabilities, namely, they are directed to predicting transaction patterns.
It would have been recognized that applying the known technique of the computer model being a multi-dimensional regression model, as taught by Kunwar, to the teachings of Durazo Almeida in view of Iscen 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 references into similar methods. Further, adding the modification of the computer model being a multi-dimensional regression model, as taught by Kunwar, into the method of Durazo Almeida in view of Iscen would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for selection of an optimal pattern (Kunwar, abstract).
Regarding claim 4, Durazo Almeida in view of Iscen teaches the method of claim 1.
Durazo Almeida in view of Iscen does not teach the computer model being one or more of: a random forest model or a linear regression model.
Kunwar, however, teaches predicting transaction patterns (i.e. abstract), including the known technique of the computer model being one or more of: a random forest model or a linear regression model (Kunwar, see at least: “The financial-transaction-pattern-prediction-based ML-model used for predicting the financial transaction payment pattern in the data prediction module uses a regression-based ML-model. In another embodiment, the regression-based ML-model may include one or more of simple linear regression models, multiple linear regression models, polynomial regression models, support vector regression models, decision tree regression models, random forest regression models [i.e. wherein the computer model is one or more of: a random forest model or a linear regression model], and the like” [0015]). This known technique is applicable to the method of Durazo Almeida in view of Iscen as they both share characteristics and capabilities, namely, they are directed to predicting transaction patterns.
It would have been recognized that applying the known technique of the computer model being one or more of: a random forest model or a linear regression model, as taught by Kunwar, to the teachings of Durazo Almeida in view of Iscen 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 references into similar methods. Further, adding the modification of the computer model being one or more of: a random forest model or a linear regression model, as taught by Kunwar, into the method of Durazo Almeida in view of Iscen would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for selection of an optimal pattern (Kunwar, abstract).
Claims 11 and 12 recite limitations directed towards a computer program product. The limitations recited in claims 11 and 12 are parallel in nature to those addressed above for claims 3 and 4, respectively, and are therefore rejected for those same reasons set forth above in claims 3 and 4, respectively.
Claim 19 recites limitations directed towards a computer program product. The rest of the limitations recited in claim 19 are parallel in nature to those addressed above for claim 3, and are therefore rejected for those same reasons set forth above in claim 3.
Claims 6, 8, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Durazo Almeida, in view of Iscen, in further view of Van Horne et al. (US 2022/0114648 A1), hereinafter Van Horne.
Regarding claim 6, Durazo Almeida in view of Iscen teaches the method of claim 1. Durazo Almeida further discloses:
-receiving a receipt associated with a customer order, wherein the receipt comprises one or more textual receipt item labels and the customer order is associated with a set of order item identifiers (Durazo Almeida, see at least: “At block 102, a service provider receives an image 112 of a receipt 114 from a user device of a user [i.e. receiving a receipt associated with a customer order]. In some embodiments, the receipt 114 is a physical receipt provided as a transaction record for a transaction between the user and a merchant at a physical location of the merchant. In some embodiments, the user captures the image 112 using a camera of the user device and transmits the image 112 to the service provider” Col. 5 Ln. 44-51 and “The receipt 114 may include a plurality of lines of text documenting various aspects of the transaction, as noted above. The text may correspond to one or more of a merchant identifier, a merchant location identifier, a date of the transaction, a time of day of the transaction, an amount of savings applied to the cost of a purchased item or applied in the transaction overall, a name of an employee, a total item count, a total pre-tax transaction amount, a total tax amount, a total transaction amount … and/or information (e.g., price, quantity, description, etc.) for each individual item [i.e. wherein the receipt comprises one or more textual receipt item labels] purchased as part of the transaction” Col. 5 Ln. 65-67 & Col. 6 Ln. 1-12 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items [i.e. the customer order is associated with a set of order item identifiers]. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas” Col. 7 Ln. 1-12);
-based on the mapping, identifying one or more predicted order item identifiers corresponding to the one or more textual receipt item labels (Durazo Almeida, see at least: “The mapping component 220 receives the extracted text from the text analysis component 218 or text extraction component 216 and generates a list of keywords for the purchased items. The mapping component 220 may also identify item identifiers of catalog items that are the same as, or that are similar to, the receipt item [i.e. based on the mapping, identifying one or more predicted order item identifiers corresponding to the one or more textual receipt item labels]. In at least one example, the mapping component 220 includes a data model(s) and a training component that can train the data model(s). The mapping component 220 can identify keywords of the extracted text using the data model(s), trained with data from a training database 228. In at least one example, the data model(s) can be trained utilizing machine learning mechanisms in which input data (for instance, in the training database 228) includes text extracted from historical receipts” Col. 13 Ln. 1-15 and “the data model may determine that, if a receipt includes “MegaGrocery” and “WW,” “WW” is an abbreviation for “whole wheat” because of training data that indicates that MegaGrocery has used “WW” as a description word for “whole wheat” items [i.e. identifying one or more predicted order item identifiers corresponding to the one or more textual receipt item labels]. As another example, the word “6” being adjacent to “$6.00” in the extracted text 116 adjacent to “orgbanana” can increase a confidence score that the keywords “organic banana” are correct based on training data indicating that an average price for an organic banana is $1.00, and so $6.00 would be a predicted price for six organic bananas” Col. 7 Ln. 1-12); and
-determining a correspondence between the one or more predicted order item identifiers to the set of ordered items (Durazo Almeida, see at least: “the service provider applies a data model to the extracted text 116 to obtain a digital list of keywords 118 and corresponding confidence scores 120. Keywords may be listed in association with confidence scores 120. A confidence score (alternatively referred to herein as a confidence value) represents the service provider's determination of a probability that the keyword(s) is an accurate reflection of a receipt item [i.e. determining a correspondence between the one or more predicted order item identifiers to the set of ordered items]. In some embodiments, the service provider may compare the confidence score to a threshold confidence score” Col. 6 Ln. 28-37).
Durazo Almeida in view of Iscen does not teach that the customer order comprises a set of order item identifiers
Van Horne, however, teaches performing image processing on a receipt (i.e. abstract), including the known technique of the customer order comprising a set of order item identifiers (van Horne, see at least: “The online concierge system 102 is configured to receive orders from one or more customers 104 (only one is shown for the sake of simplicity). An order specifies a list of goods (items or products) [i.e. the customer order comprises a set of order item identifiers] to be delivered to the customer 104” [0015] and “The order fulfillment engine 206 interacts with the image processing module 216 to adjust an estimated cost of an order based on an image of a receipt that contains actual amounts purchased of items. In one embodiment, the order fulfillment engine 206 determines an estimated price of the order based on ordered quantities of items [i.e. the customer order comprises a set of ordered item identifiers]. Upon receiving an image of the receipt for the order, the image processing module 216 determines a price adjustment based on the difference between an ordered amount and an actual amount purchased of each item” [0022]). This known technique is applicable to the method of Durazo Almeida in view of Iscen as they both share characteristics and capabilities, namely, they are directed to performing image processing on a receipt.
It would have been recognized that applying the known technique of the customer order comprising a set of order item identifiers, as taught by Van Horne, to the teachings of Durazo Almeida in view of Iscen 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 references into similar methods. Further, adding the modification of the customer order comprising a set of order item identifiers, as taught by Van Horne, into the method of Durazo Almeida in view of Iscen would have been recognized by those of ordinary skill in the art as resulting in an improved method that would improve accuracy of a determining an item quantity (Van Horne, [0001]).
Regarding claim 8, the combination of Durazo Almeida/Iscen/Van Horne teaches the method of claim 6.
Durazo Almeida in view of Iscen does not teach determining a discrepancy between the one or more predicted order item identifiers and the set of ordered items; and flagging the customer order for manual review.
Van Horne, however, teaches performing image processing on a receipt (i.e. abstract), including the known technique of determining a discrepancy between the one or more predicted order item identifiers and the set of ordered items (van Horne, see at least: “If the quantity 646 identified by the image processing module 216 is incorrect (i.e., does not match the actual amount purchased as printed on the receipt) [i.e. determining a discrepancy between the one or more predicted order item identifiers and the set of ordered items], the picker 108 uses the input mechanism 654 to manually input the actual amount purchased of the second item 640” [0073]); and
the known technique of flagging the customer order for manual review (van Horne, see at least: “The user interface 620 includes a current prompt 626 prompting the picker 108 to check that each identified measured quantity [i.e. flagging the customer order for manual review] is the actual amount purchased of each item. That is, the picker 108 is checking that the image processing module 216 correctly identified the actual amount purchased of each item. The user interface 620 further includes a first item 630 for which the picker has been prompted to check that the measured quantity is the actual amount purchased” [0068] Examiner notes that each measured quantity is flagged for manual review). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Durazo Almeida in view of Iscen with Van Horne for the reasons identified above with respect to claim 6.
Claims 14 and 16 recite limitations directed towards a computer program product. The limitations recited in claims 14 and 16 are parallel in nature to those addressed above for claims 6 and 8, respectively, and are therefore rejected for those same reasons set forth above in claims 6 and 8, respectively.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Durazo Almeida, in view of Iscen, in further view of Van Horne, in further view of Leizerowich et al. (US 2020/0057903 A1), hereinafter Leizerowich.
Regarding claim 7, the combination of Durazo Almeida/Iscen/Van Horne teaches the method of claim 6. Durazo Almeida further discloses:
-wherein determining a correspondence between the one or more predicted order item identifiers to the set of ordered items (Durazo Almeida, see at least: “the service provider applies a data model to the extracted text 116 to obtain a digital list of keywords 118 and corresponding confidence scores 120. Keywords may be listed in association with confidence scores 120. A confidence score (alternatively referred to herein as a confidence value) represents the service provider's determination of a probability that the keyword(s) is an accurate reflection of a receipt item [i.e. determining a correspondence between the one or more predicted order item identifiers to the set of ordered items]. In some embodiments, the service provider may compare the confidence score to a threshold confidence score” Col. 6 Ln. 28-37) comprises:
-identifying at least two predicted order item identifiers corresponding to a textual receipt item label of the one or more textual receipt item labels (Durazo Almeida, see at least: “the graphical user interface 400 may include several catalog items as matches for a particular receipt item. The graphical user interface 400 may indicate the most highly correlated catalog item (e.g., as determined by a confidence score, number of matching attributes to the receipt item, etc.) and one or more alternative or substitute catalog items … multiple catalog items identified as matches for a single receipt item may be placed in an order [i.e. identifying at least two predicted order item identifiers corresponding to a textual receipt item label of the one or more textual receipt item labels]. In some examples, the order may be determined based on user preferences, specific items requested, alphabetic order, price order (e.g., high to low, low to high), promotions, availability of the catalog item, etc. Using the example in the preceding paragraph, regardless of whether the brand of black beans was or was not available from the service provider 202 (e.g., was or was not included in the catalog of items maintained by the service provider 202), the service provider 202 could list multiple brands of black beans, canned beans of a different variety (e.g., kidney beans, garbanzo beans, etc.), [i.e. identifying at least two predicted order item identifiers] and so on” Col. 18 Ln. 19-40).
The combination of Durazo Almeida/Iscen/Van Horne does not explicitly teach, responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label.
Leizerowich, however, teaches extracting information from a receipt (i.e. abstract), including the known technique of, responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label (Leizerowich, see at least: “for each of a plurality of predetermined features, product determination functionality 160 assigns a value to each of the determined candidate product identifiers of stages 2045-2050 such that the relevancy of the different identified candidate product identifiers can be compared [i.e. determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label]” [0076] and “the features are selected from the following group of features: … a) the rank of the determination of the candidate product identifier in stage 2045, i.e. the level of confidence that the match of the first text portion abbreviation and the product name abbreviation is indicative of similar product names, as described above in relation to stage 1080; … b) whether or not a product associated with the identified candidate product identifier was sold in the issuing outlet in a first predetermined previous number of days; [i.e. responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label] … c) whether or not a product associated with the identified candidate product identifier was sold in the identified related outlets of stage 2030 in a second predetermined previous number of days, optionally the same as the first predetermined previous number of days” [0077-0080] and “In stage 2090, responsive to the assigned values of stage 2080 [i.e. responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label], product determination functionality 170 assigns a candidate score to each of the identified candidate product identifiers [i.e. determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label] of stages 2045-2050” [0083]). This known technique is applicable to the method of the combination of Durazo Almeida/Iscen/Van Horne as they both share characteristics and capabilities, namely, they are directed to extracting information from a receipt.
It would have been recognized that applying the known technique of, responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, as taught by Leizerowich, to the teachings of the combination of Durazo Almeida/Iscen/Van Horne 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 references into similar methods. Further, adding the modification of, responsive to determining a discrepancy between a first predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, determining a correspondence between a second predicted order item identifier of the at least two predicted order item identifiers and the textual receipt item label, as taught by Leizerowich, into the method of the combination of Durazo Almeida/Iscen/Van Horne would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow for a quicker and more accurate identification of product candidates (Leizerowich, [0050]).
Claim 15 recites limitations directed towards a computer program product. The limitations recited in claim 15 are parallel in nature to those addressed above for claim 7, and are therefore rejected for those same reasons set forth above in claim 7.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that the claims recite additional elements that amount to significantly more than an abstract idea and provide a specific improvement to computer technology. As described in paragraphs [0006]-[0007] of the specification, the claimed computer system implements a trained model that learns a many-to-many association between textual receipt item labels, which may vary in syntax, formatting, or abbreviation, and order item identifiers corresponding to items known to exist in a particular retail or warehouse environment. The model may be trained using unsupervised learning over historical order-receipt pairs to infer the underlying relationships between the disparate textual receipt data and structured identifiers of items within the environment. Conventional systems rely on manually curated look-up tables or heuristic string matching, which fail when receipt text is inconsistent, ambiguous, or incomplete. The present invention instead enables a computer system to automatically infer these mappings at scale (Remarks, pages 9-10).
Examiner respectfully disagrees. Merely implementing a model that utilizes many-to-one does not improve the model itself, it applies the machine learning model to the abstract idea rather than actually improving the functioning of a computer or an improvement to another technology or technical field. Additionally, identifying items in an order receipt is not a technical field and providing more consistent, less ambiguous, and more complete matches is not a technical improvement, rather, it’s an improvement to the abstract idea. Accordingly, the amended claims are ineligible.
Applicant further argues that the claims further describe applying a diagonal matrix with one-hot encodings for individual textual receipt item labels to the trained model to obtain predicted ordered item identifiers corresponding to each textual receipt item label. The one-hot encoding within the diagonal matrix zeroes out all other receipt labels, allowing the model to isolate the contribution of the specific textual receipt item label. This transformation converts a many-to-many learned representation into explicit, per-label mappings between receipt text and structured identifiers, thereby improving the interpretability and precision of automated reconciliation between unstructured textual inputs and known inventory items. This approach also mitigates the cold-start problem, since the system can infer likely ordered item identifiers for a textual receipt label even when there is sparse or no direct historical mapping for that textual receipt item label. By isolating predictions for one textual receipt item label at a time, the model reduces the effect of noisy or incomplete mappings and enables reliable per-label predictions even in the presence of sparse or inconsistent training data (Remarks, pages 10-11).
Examiner respectfully disagrees. Improving the interpretability and precision of reconciliation between unstructured textual inputs and known inventory items, mitigating a cold-start problem, reducing the effect of noisy or incomplete mappings, and enabling reliable per-label predictions even in the presence of sparse or inconsistent training data are not a technical solutions as they improve the abstract idea rather than improving the functioning of a computer or an improvement to another technology or technical field. Merely utilizing additional elements such as one-hot encoding amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea). Additionally, matching items from an order receipt is not a technical field. Accordingly, the amended claims are ineligible.
Applicant further argues that the claimed features directly enhances the performance of computer systems performing automated text-to-identifier reconciliation, improving both accuracy and generalization in a way that conventional rule-based systems cannot. These improvements are rooted in computer technology and represent a concrete enhancement to the way computers process and reconcile unstructured textual data with structured environmental identifiers (Remarks, page 11).
Examiner respectfully disagrees. Improving accuracy and generalization of matching items from an order receipt is not a technical improvement nor is it ‘rooted in computer technology,’ and ‘rules based systems’ are not a technical field. Merely applying the recited additional elements does not improve the functioning of a computer or an improvement to another technology or technical field. Additionally, Applicant has not described any technical way in which the computers process and reconcile unstructured textual data with structured environmental identifiers is improved. MPEP 2106.05(a) states that “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification … if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” Accordingly, the amended claims are ineligible.
Applicant further argues that, in view of the above, claim 1 recites patent-eligible subject matter, as do claims 9 and 17, which recite similar subject matter to claim 1. The dependent claims are also patent-eligible by virtue of their dependency (Remarks, page 11).
Examiner respectfully disagrees. As detailed in response to the arguments above, claim 1 is ineligible. Accordingly, claims 9 and 17, as well as the dependent claims, are ineligible.
Rejections under 35 U.S.C. §103
Applicant argues that Downey does not teach the amended features of the claims and Durazo Almeida does not appear to remedy these deficiencies of Downey (Remarks, pages 11-12).
Applicant’s argument has been considered but is moot because this arguments does not apply to the current combination of references being used to the argued amended features.
Applicant further argues that, in view of the above, claim 1 is patentable over the cited references. Claims 9 and 17, which recite similar subject matter to claim 1, are also patentable over the cited references. The dependent claims are patentable by virtue of their dependency. Thus, the rejection should be withdrawn (Remarks, page 12).
Examiner respectively disagrees. Amended claim 1 is taught by Durazo Almeida in view of newly cited Iscen. Accordingly, claims 9 and 17, as well as the dependent claims, are rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Kumar et al. (US 2024/0312233 A1) teaches information extraction from an image.
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 ARIELLE E WEINER whose telephone number is (571)272-9007. The examiner can normally be reached M-F 8:30-5:00.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689