DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment filed on 09/18/2026. Claims 1-25 are pending. The effective filing date of the claimed invention is 09/03/2024.
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 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 1-25 are rejected under 35 U.S.C. 101 because the claims are found to be directed to abstract idea.
Step 1 – Claims 1-15 are process claims; claims 16-21 are manufacture claims; and, claims 22-25 are machine claims. Step 1 is satisfied.
Step 2A Prong 1 – Exemplary claim 1 (and similarly claim 16, 22) is found to recite abstract idea. Claim 1 recites the following abstract idea: method comprising:
causing display of at least part of a first table of data and at least part of a second table of data, wherein the at least part of the first table is displayed for matching against the second table (e.g. MPEP 2106.04(a)(2)(III)(A) citing Electric Power Group);
for a first record of the first table, combining a first plurality of values of the first record, and generating a first vector embedding of the first plurality of values as combined (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721; MPEP 2106.04(a)(2)(III));
for each set of one or more second records of a plurality of second records of the second table, combining a second plurality of values of the one or more second records into one or more combined sets of values, and generating one or more second vector embeddings of the one or more combined sets of values (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721; MPEP 2106.04(a)(2)(III));
for each particular one or more second vector embeddings of a plurality of second vector embeddings generated from the generating the one or more second vector embeddings, determining a distance between the first vector embedding and the particular one or more second vector embeddings; wherein at least a first set of one or more second records has a closer distance than at least a second set of one or more second records; wherein determining the distance uses a trained machine learning model that applies different weights for different component-level differences between the first vector embedding and the particular one or more second vector embeddings (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721; MPEP 2106.04(a)(2)(III));
based at least in part on determining the distance, graphically marking the first set of one or more second records with a higher confidence than the second set of one or more second records (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721; MPEP 2106.04(a)(2)(III) pen and paper); and
causing display of an option to select the first set or the second set as a match for the first record, wherein using the option causes the selected set to be stored in association with the first record and causing generation of one or more reconciliation entries in association with one or more of the first table and the second table to account for a difference between the first record and the selected set (see MPEP 2106.04(a)(2)(II)(C) Another example of a claim reciting social activities is Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of “’providing information to a person without interfering with the person’s primary activity.’” 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)). The patentee claimed an attention manager for acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content. 896 F.3d at 1339-40, 127 USPQ2d at 1555. The Federal Circuit concluded that “[s]tanding alone, the act of providing someone an additional set of information without disrupting the ongoing provision of an initial set of information is an abstract idea,” observing that the district court “pointed to the nontechnical human activity of passing a note to a person who is in the middle of a meeting or conversation as further illustrating the basic, longstanding practice that is the focus of the [patent ineligible] claimed invention.” 896 F.3d at 1344-45, 127 USPQ2d at 1559.).
When these limitations are viewed alone and in ordered combination, the examiner finds claim 1 (and 16, 22) to recite abstract idea.
Step 2A Prong 2 – Claim 1 (and 16, 22) are not found to integrate the abstract idea into practical application. Exemplary claim 1 recites the following additional elements: “computer-implemented method” in preamble, providing option on display for selection, storing data, etc. See MPEP 2106.05(f) “apply it” rationale. When viewed alone and in ordered combination, claim 1 is found to be directed to abstract idea.
Step 2B – Claim 1 (and 16, 22) are not found to include significantly more. The additional element analysis from Step 2A Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional (WURC) activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. In this regard, the courts have recognized the following computer functions as WURC functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Claim 1 includes limitations relating to receiving and transmitting data over a network. This was found to be WURC activity. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)
Claim 1 includes limitations of storing and retrieving data, where this has been found to be WURC. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim 1 includes limitations relating to making a selection on web browser and going back and forth between different pages and associated functionality. This was found to be WURC activity. See Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015).
When viewed alone and in ordered combination, these limitations are not found to include significantly more, and therefore claim 1 (and 16, 22) are directed to abstract idea.
Dependent Claims – Claim 2 recites more abstract idea. See MPEP 2106.04(a)(2)(I). Claim 3 recites more abstract idea. Id. Claim 4 recites more abstract idea. Id. Claim 5, 17, 18, 23 recites more abstract idea, performed in apply it manner with WURC functionality. See MPEP 2104.02(a)(2)(III); Recentive v. Fox, Appeal No. 2023-2437 (Fed. Cir. 04/18/2025). Claim 6 is more abstract idea. See MPEP 2106.04(a)(2)(II)(C). Claim 7, 19, 25 recite more abstract idea. See MPEP 2106.04(a)(2)(I). Claim 8, 9, 20 recite more abstract idea with WURC functionality of the webpage. See above. Claim 10, 21 recite more abstract idea. See MPEP 2106.04(a)(2)(I)(A) manipulating information. Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: iii. Mere automation of manual processes. Claims 11-15, 24 are more abstract idea performed in apply it manner. See MPEP 2106.04(a)(2)(I); MPEP 2106.05(f); and, Recentive v. Fox.
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.
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.
Claim(s) 1-7, 11-19, 22-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. Pub. No. 2021/0350382 to Lopes et al. (“Lopes”) in view of U.S. Pat. No 11,928,423 to Eliyahu et al. (“Eliyahu”).
With regard to claims 1, 16, 22, Lopes discloses the claimed computer-implemented method comprising:
causing display of at least part of a first table of data and at least part of a second table of data, wherein the at least part of the first table is displayed for matching against the second table (Lopes e.g. Fig 2A, [0044] [0033]);
for a first record of the first table, combining a first plurality of values of the first record (Lopes e.g. [0046, 49] As shown in FIG. 2B, the transaction reconciliation system may combine pairs of transactions into transaction pairs 220, and may create entries for each transaction pair 220), and generating a first vector embedding of the first plurality of values as combined (see Lopes e.g. [0053, 55] matrix matrices and [0046, 49] for the combining; However, Lopes does not disclose the specifics of any vector embedding; Eliyahu teaches this at e.g. col. 3, ln. 15-50 - In some aspects, an embedding model produces embeddings for entities and transactions. The embedding for each individual entity may comprise, for example, a vector of numerical values in a multi-dimensional space in which dimensions represent features or attributes of an individual element in the context of an entity to transaction mapping. Similarly, a transaction embedding may comprise a vector of numerical values in a multi-dimensional space in which the dimensions represent features or attributes of the transaction in the context of an entity to transaction mapping., col. 4, ln. 45-60);
for each set of one or more second records of a plurality of second records of the second table, combining a second plurality of values of the one or more second records into one or more combined sets of values, and generating one or more second vector embeddings of the one or more combined sets of values (Lopes e.g. [0046, 49]; However, Lopes does not disclose the specifics of any vector embedding; Eliyahu teaches this at e.g. col. 3, ln. 15-50 - In some aspects, an embedding model produces embeddings for entities and transactions. The embedding for each individual entity may comprise, for example, a vector of numerical values in a multi-dimensional space in which dimensions represent features or attributes of an individual element in the context of an entity to transaction mapping. Similarly, a transaction embedding may comprise a vector of numerical values in a multi-dimensional space in which the dimensions represent features or attributes of the transaction in the context of an entity to transaction mapping., col. 4, ln. 45-60, Eliyahu further teaches where the embedding is performed at least for each entity in an entity list);
for each particular one or more second vector embeddings of a plurality of second vector embeddings generated from the generating the one or more second vector embeddings, determining a distance between the first vector embedding and the particular one or more second vector embeddings (Lopes does not disclose this; Eliyahu does at e.g. col 11 ln 44-50, In one embodiment, cosine similarity can be utilized to measure the similarity between embedding vectors., Eliyahu Abstract, The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.); wherein at least a first set of one or more second records has a closer distance than at least a second set of one or more second records (Lopes does not explicitly disclose this; Eliyahu, e.g. abstract, A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects. Where higher similarity score is closer distance); wherein determining the distance uses a trained machine learning model that applies different weights for different component-level differences of a plurality of component-level differences from the first plurality of values (see Lopes [0055] feature information used to retrain model; [0028] The transaction grouping model may be a trained machine learning model, such as an LSTM machine learning model, an RNN machine learning model, a graph-based machine learning model, a Louvain model, and/or the like.; [0057] weight graph edges based on probabilities, An edge may be associated with a weight that indicates a strength of a relationship between nodes, which may be equal to or determined based on the probability that a transaction entry pair is associated with the same transaction. Thus, a first node may represent a first transaction entry, a second node may represent a second transaction entry, an edge between the first node and the second node may indicate a relationship between the first transaction entry and the second transaction entry, and a weight associated with the edge may represent a probability that the first transaction entry and the second transaction entry are associated with the same transaction; Lopes does not explicitly disclose a first vector embedding, one or more second vector embeddings, and component-level differences between those embeddings; see Eliyahu at e.g. Fig. 11, element 1140 compute the transaction embedding1 (i.e. first vector embedding), Fig. 11 element 1150 refers to other entity embedding and associated similarity scores (inherently one or more second vector embeddings) and component level differences between those embeddings is shown at Eliyahu Fig. 11, element 1160 where the similarity score of the first vector and the second vector(s) is shown and the closest in distance is returned at 1160. );
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based at least in part on determining the distance, graphically marking the first set of one or more second records with a higher confidence than the second set of one or more second records (for ranking candidate sets, see Lopes at [0030] [0056], [0030] The transaction reconciliation system may apply the transaction grouping model (e.g., a machine learning model) to calculate probabilities for different groups of transaction entries and/or to create clusters of transaction entries. [0032] For example, the transaction reconciliation system may mark (e.g., by updating a field of transaction entries, by storing information in memory and/or in a data structure, and/or the like) the classified entries and the matched entries as reconciled. Fig. 2D showing a graphical marking of the higher/lower confidence probability of the pairs); and
causing display of an option to select the first set or the second set as a match for the first record, wherein using the option causes the selected set to be stored in association with the first record and causing generation of one or more reconciliation entries in association with one or more of the first table and the second table to account for a difference between the first record and the selected set (see Lopes, e.g. [0034] A user may review the reconciled transactions, and may confirm or reject the reconciled transactions. If the user provides input confirming the reconciled transactions, the transaction reconciliation system may update a data structure to mark the transactions as reconciled, to include predicted transaction data, and/or the like, as described elsewhere herein. If the user provides input rejecting the reconciled transactions, . . . ; for storing selected match, see Lopes [0022] [0034]; for generating reconciliation entries, see Lopes [0022] [0034]; for generating reconciliation entries, see Lopes at [0037] update a transaction record with predicted transaction data, [0038] add transaction entry to a group and reconcile the transaction, and [0034] update data structures to include predicted transaction data). Therefore, it would have been obvious to one of ordinary skill in the transaction reconciliation art to modify the system of Lopes to include e.g. generating transaction embeddings, generating entity embeddings, comparing embeddings, similarity/distance scores, and selecting highest scoring candidates, as taught by Eliyahu, for the following beneficial reasons:
Eliyahu – col. 3, ln. 9-15 - The trained machine learning model thus exploits context information, for example in a transaction record, to perform improved entity identification, which is a beneficial technical effect. For example, such a trained machine learning model may be used to perform improved automated data entry in any application requiring data entry.
Eliyahu – col. 3 ln. 29-45 - Beneficially, capturing meaning and context information with embeddings produced by trained machine learning models results in improved matching performance between entities and transactions. Moreover, aspects described herein pre-embed entity information such that the matching process may be performed rapidly and automated data entry may be performed in real-time. The computational complexity of conventional word matching (and fuzzy matching) techniques generally precludes the possibility of real-time matching and automated data entry. Additional aspects described herein relate to further optimizations that improve matching speed, another beneficial technical effect. For example, candidate entities can be filtered to a subset of entities having a relation or association with the transacting entity. Further yet, dimensionality reduction techniques may be applied to embeddings generated by the machine learning models described herein to enable more efficient storage, faster retrieval, and faster matching with lower computational complexity.
Eliyahu – col. 3 ln 12-15 - For example, such a trained machine learning model may be used to perform improved automated data entry in any application requiring data entry.
Eliyahu – col. 7 ln 3-10 - the entity embeddings could be determined when a call is made to predict an entity, but by preprocessing the entities and storing them, they can be quickly accessed, and overall speed is improved. Further, using a high-speed database for these embeddings can further improve response time.
With regard to claim 2, Lopes further discloses the one or more conditions check whether a value of the set of one or more second records when aggregated with zero (under BRI the examiner finds zero) or more other records of the set of one or more second records to be the same or within a threshold amount of a corresponding value of the first record (see e.g. Fig. 2D, [0082] [0083] etc.).
With regard to claim 3, Lopes further discloses the corresponding value of the first record is a transaction amount (see e.g. [0014]).
With regard to claim 4, Lopes does not disclose and Eliyahu teaches at e.g. col. 11 ln. 44-50, determining the distance comprises determining a cosine distance between the first vector embedding and the one or more second vector embeddings. See motivation to combine with regard to claim 1, above.
With regard to claims 5, 17, 18, 23, Lopes further discloses causing display of another option to select another of the first set or the second set as a non-match for the first record, wherein using the other option causes the other selected to be provided as negative feedback to the trained machine learning model, and wherein the trained machine learning model updates one or more of the weights based at least in part on the negative feedback (see e.g. [0048] [0056] [0149-150]).
With regard to claim 6, Lopes further discloses in response to using the option, providing the selected set and the first record as a labeled pair of positive feedback to the trained machine learning model; wherein using the option causes the trained machine learning model to update one or more of the weights based at least in part on the labeled pair of positive feedback for the selected set and the first record (see e.g. [0048] [0056] [0149-150]).
With regard to claims 7, 19, 25, Lopes further discloses determining a distance between the first vector embedding and the particular one or more second vector embeddings comprises determining a first distance between the first vector embedding and a third vector embedding and determining a second distance between the first vector embedding and a fourth vector embedding; and aggregating the first distance and the second distance to generate the distance (Lopes discloses the multi-record / grouped transaction side. Lopes determines probabilities for pairs of unmatched transaction entries and then generates a graph where nodes represent transaction entries and weighted edges represent the probability that transaction-entry pairs belong to the same transactions. Lopes then clusters/groups transaction entries using the graph/probability table. Eliyahu supplies the embedding-comparison concept, generating transaction/entity embeddings and comparing embeddings using similarity scores/cosine similarity. See combination above.).
With regard to claim 11, Lopes further discloses trained machine learning model uses a random forest technique to randomly generate decision trees based on subsets of training data, use the decision trees to predict matches for the first record, and aggregating results of the decision trees to determine the higher confidence of the first set than the second set (e.g. [0073]).
With regard to claim 12, Lopes further discloses the trained machine learning model uses a neural network with a plurality of layers; wherein a layer of the neural network reduces a number of candidate matches of the second table based on transaction amounts and another layer of the neural network outputs the higher confidence of the first set than the second set (e.g. [0019] [0079]).
With regard to claim 13, Lopes further discloses the trained machine learning model uses a decision tree with a plurality of branches; wherein a branch of the decision tree reduces a number of candidate matches of the second table based on transaction amounts and another branch of the decision tree outputs a classification that contributes to the higher confidence of the first set than the second set (e.g. [0073-74]).
With regard to claim 14, Lopes further discloses the trained machine learning model uses a naïve bayes algorithm to determine the higher confidence of the first set than the second set based on a historical probability of other sets of records being labeled as matches to records based on similar characteristics than were used between the first set and the first record (e.g. [0079]).
With regard to claim 15, Lopes further discloses the trained machine learning model uses a generalized linear model to determine the higher confidence of the first set than the second set based on a historical distribution of sets of records matched to records based on characteristics similar to the first record (e.g. [0073]).
With regard to claim 24, Lopes further discloses using the option causes the trained machine learning model to update one or more of the weights based at least in part on positive feedback for the selected set (e.g. [0047]).
Claim(s) 8, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopes, Eliyahu, and US Pat Pub No 2021/0398399 to Hunt et al. (“Hunt”).
With regard to claims 8, 20, Lopes/Eliyahu does not disclose the drop down input function. See Hunt at e.g. [0024] [0030] [0037]. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Lopes/Eliyahu to include such drop down menu as input function, as this is simply another method of showing and inputting data, providing the user with another way to design the system so that the user can only input the drop down menued items.
Claim(s) 9, 10, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopes, Eliyahu, and US Pat Pub No 2022/0405836 to Gorman et al. (“Gorman”).
With regard to claim 9, Lopes and Eliyahu does not teach claim 9. However, Gorman teaches causing display of the option to select the first set or the second set as the match for the first record is performed in response to selection of the first record for matching (Gorman, Fig. 2A, [0039]), the computer-implemented method further comprising: selecting a third record of the first table for an alternative matching process (Gorman e.g. [0040-42], Fig 2A-2B), and causing concurrent display of: another option to perform the alternative matching process for the third record (Gorman e.g. Fig 2A [0040] expressly identifies the buttons including the next match button and skip button; [0044]), and content describing why the third record should be matched before the first record (Gorman e.g. [0040] The suggested reconciliation 214 further includes buttons for selecting an action by the user and an informational message about the transaction (e.g., “two other possible matches found”).). Therefore, it would have been obvious to one of ordinary skill in the art to modify Lopes, Eliyahu to include such multiple records and information so that this provides options to the user(s) and further explains the situation to the user(s).
With regard to claims 10, 21, See Lopes [0056-58] Fig 2D-2E. Gorman teaches the reconciliation UI, multiple transactions displayed, suggested reconciliations displayed next to transactions (for the claimed automatic marking), and match tab, find match, and multiple suggestions, as shown throughout Gorman. Therefore, it would have been obvious to one of ordinary skill in the art to modify Lopes, Eliyahu to include such multiple records and information so that this provides options to the user(s) and further explains the situation to the user(s).
Response to Arguments
Applicant's arguments filed 09/18/2026 have been fully considered but they are not persuasive.
Applicant argues that the claims are eligible under 35 USC 101. The examiner respectfully disagrees. The examiner has addressed the amendments above and does not find them to overcome the 101 rejection as they are still directed to abstract idea. The amended language even includes abstract idea of mathematical concepts.
Next, for the prior art, the examiner respectfully disagrees that the cited art does not teach the amended limitations. The examiner has referred to the combination of primary/secondary to teach these limitations.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
1 See Eliyahu, abstract, “an entity embedding, comprising a vector.”
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