DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 27 November 2024.
Claims 1-20 are pending and have been examined.
Allowable Subject Matter
Claims 3-11 and 17-18 recite allowable subject matter, and would be allowable if rewritten to overcome the remaining rejections under 35 U.S.C. 103 and 35 U.S.C. 101 set forth in this Office Action and to include all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea without significantly more).
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-14 are directed to a method, claims 15-19 are directed to an article of manufacture (non-transitory computer readable storage medium), and claim 20 is directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of generating and displaying information about a plurality of orders. Specifically, representative claim 1 recites the abstract idea of:
receiving, from a request including an aggregated list of items and an identity of a user of the system associated with the aggregated list;
responsive to the received request, generating an order including the aggregated list of items;
identifying, based at least in part on a catalog of items of a single source, that the order is unserviceable by the single source;
responsive to identifying that the order is unserviceable, generating, for each of the items in the aggregated list, an embedding vector of a plurality of embedding vectors, wherein the embedding vector is generated by:
retrieving, from the system, information about past orders placed at the online system,
deriving, using the retrieved information, cooccurrence data for each of the items in the aggregated list including information about cooccurrences in the past orders of each of the items in the aggregated list with other items in the aggregated list, and
including, using the cooccurrence data, an indication about a cooccurrence of each of the items in the aggregated list with a corresponding other item in the aggregated list into a corresponding dimension of the embedding vector thereby reducing a first dimensionality of the cooccurrence data to a second dimensionality of the embedding vector;
clustering, using the plurality of embedding vectors for the items in the aggregated list, the aggregated list of items to a plurality of clusters of items, each of the plurality of clusters serviced by a different source;
generating, using order data for the user, a plurality of orders by including one or more items from each of the plurality of clusters to a respective order of the plurality of orders and assigning a different source of a plurality of sources to the respective order;
generating, using the plurality of orders; and
sending, to the user, wherein the sending causes associated with the user to display information about the plurality of orders and the plurality of sources for servicing the plurality of orders.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of generating and displaying information about a plurality of orders, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claim 1 is a certain method of organizing human activity because it relates to sale activities since the claims specifically recite steps involved in the generating and sending of the information about the plurality of orders, where the steps include identifying based on a catalog items that the order is unserviceable by a single source and in response to the identifying information is generated for each item in an aggregated list, thereby making this a sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: a computer system comprising a processor and a computer-readable medium, an online platform and via an interface of an online system, a signal, the signal, a database of the online system, the online system, a user interface signal, via a network, the user interface signal to a device, the device, and a user interface.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., generating and displaying information about a plurality of orders) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Additionally, the Examiner notes that the claim recites the step sending, via a network, the user interface signal to a device associated with the user, wherein the sending causes the device associated with the user to display a user interface with information about the plurality of orders and the plurality of sources for serving the plurality of orders, which is considered to be insignificant extra-solution activity. Extra-solution activity can be understood as activities that are incidental to the primary process or product that are merely a nominal or tangential addition the to claim (see MPEP 2106.05(g)). In this case, the activity of sending, via a network, information about the plurality of orders is merely nominal or tangential additions to the primary process of generating and displaying information about a plurality of orders.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Further, the step of sending, via a network, the user interface signal to a device associated with the user, wherein the sending causes the device associated with the user to display a user interface with information about the plurality of orders and the plurality of sources for serving the plurality of orders, does not provide significantly more than the judicial exception because they are merely well-understood, routine, and conventional activities previously known to the industry of data management and processing. The courts have recognized the computer functions as well-understood, routine, and conventional functions when they are claimed in a generic manner or as insignificantly extra-solution activity. Receiving or transmitting data [i.e., sending] over a network (e.g., using the Internet to gather data) are recognized computer functions that are considered insignificant extra-solution activity (see MPEP 2106.05(d)(II)). This is similar to the steps and additional elements that are recited in the claims. For example, the step of sending, via a network to a device associated with the user, where the sending causes the device to display information about a plurality of orders, would be the same as the activity of transmitting and receiving data over a network in this case. For examples of court cases, see Versata Dev. Group, Inc. v. SAP Am, Inc., 793 F.3d 1306, 1344 (Fed. Cir. 2015) and Intellectual Ventures I v. Symantec Corp., 838 F. 3d 1307, 1315 (Fed. Cir. 2016).
As such, representative claim 1 is ineligible.
Independent claims 15 and 20 are similar in nature to representative claim 1, and Step 2A, Prong 1 analysis is the same as above for representative claim 1. It is noted that in independent claim 15 includes the additional elements of a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps, and independent claim 20 includes the additional element of a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform the steps. The Applicant’s specification does not provide any discussion or description of additional elements in independent claims 15 and 20, as being anything other than generic elements. Thus, under Step 2A, Prong 2 of the analysis, the claimed additional elements of claims 15 and 20 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 15 and 20 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, under Step 2B of the analysis, the additional elements of claims 15 and 20 considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 15 and 20 are ineligible.
Dependent claims 2-14 and 16-19, depending from claims 1 and 15 respectively, do not aid in the eligibility of the independent representative claim 1, nor the independent claims 15 and 20. The claims of 2-14 and 16-19 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims include the additional elements of recomputing (claims 2 & 16), a clustering machine-learning model of the online system and clustering machine-learning model is trained (claims 3, 4, 11, 17, & 18), applying the clustering machine-learning model to the embedding vector (claim 3 & 7), training, using the training data, the clustering machine-learning model (claims 5 & 6), applying the clustering machine-learning model (claim 9), re-training the clustering machine-learning model (claim 11), and user interface elements (claims 12, 14, & 19). Applicant’s specification does not provide any discussion or description of the recited additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). While the claims recite a clustering machine-learning model, the recitations are results based in nature and do not include details as to how the machine learning is actually functioning beyond known functions. Accordingly, claims 2-7, 9, 11-12, 14, and 16-18 are directed towards an abstract idea. Additionally, the additional elements of claim 2-7, 9, 11-12, 14, and 16-19 considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 8, 10, and 13 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, the dependent claims 2-14, and 16-19 are ineligible.
Claim Rejections - 35 USC § 103
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 12-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, G., et al. (PGP No. US 2022/0101250 A1), in view of Maschmeyer, R., et al., (PGP No. US 2025/0165125 A1).
Claim 1-
Wang discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, from an online platform and via an interface of an online system, a request signal including an aggregated list of items and an identity of a user of the online system associated with the aggregated list (Wang, see: paragraph [0029] “system 100” and “receiving order information 111 from the user device”; and paragraph [0033] disclosing “each user 110 may be associated with a respective delivery location 126 to which an order is requested”; and see: .and paragraph [0038] disclosing “may receive orders for items 118”);
responsive to the received request signal, generating an order including the aggregated list of items (Wang, see: paragraph [0037] disclosing “application 130 may use for presenting the order information”; and see: paragraph [0038] disclosing “may receive orders for items [i.e., aggregated list of items] 118”);
identifying, based at least in part on a catalog of items of a single source, that the order is unserviceable by the single source (Wang, see: paragraph [0039] disclosing “the merchant information 122 may include inventory information for the respective merchant 114. For instance, if a particular merchant 114 is out of a particular item 118, the item information, such as a list of…merchant items [i.e., catalog of items of a single source]” and “items that are not currently in the inventory of the particular merchant and/or may indicate that these items are not currently available for ordering”);
responsive to identifying that the order is unserviceable, generating, for each of the items in the aggregated list, (Wang, see: paragraph [0039] disclosing “the merchant information 122 may include inventory information” an “a particular merchant 114 is out of a particular item” and “items that are not currently in the inventory of the particular merchant and/or may indicate that these items are not currently available for ordering”):
retrieving, from a database of the online system, information about past orders placed at the online system (Wang, see: paragraph [0051] disclosing “order processing program 140 may store information associated with each order as past order information 148”),
deriving, using the retrieved information, cooccurrence data for each of the items in the aggregated list including information about cooccurrences in the past orders of each of the items in the aggregated list with other items in the aggregated list (Wang, see: paragraph [0060] disclosing “the bundling program 150 may initially present items” and “the past order information 148 may be used”; and paragraph [0104] disclosing “application may immediately surface bundled combinations of items that may have been determined” and “may display ‘Frequently ordered together’ bundles”), and
including, using the cooccurrence data, an indication about a cooccurrence of each of the items in the aggregated list with a corresponding other item in the aggregated list (Wang, see: paragraph [0060] disclosing “the bundling program 150 may initially present items”; and paragraph [0104] disclosing “application may immediately surface bundled combinations of items that may have been determined” and “may display ‘Frequently ordered together’ bundles”);
clustering, using the data for the items in the aggregated list, the aggregated list of items to a plurality of clusters of items, each of the plurality of clusters serviced by a different source (Wang, see: paragraph [0096] disclosing “present items offered by a selected first merchant as indicated at 602 as well as items offered by a second merchant, as indicated at 604, that are available for bundling with items ordered from the first merchant”; And see FIG. 6 rendering the interface for selecting a list of items that are aggregated, where each of the clustered plurality of items are offered by different merchants, such as The Burger Store and Bob’s Convenience Store.);
generating, using order data for the user, a plurality of orders by including one or more items from each of the plurality of clusters to a respective order of the plurality of orders and assigning a different source of a plurality of sources to the respective order (Wang, see: paragraph [0097] disclosing “generate the user interface 600, when the user 110 selects the first merchant 602 (i.e., THE BURGER STORE in this example) for…items 610 offered by the first merchant 602, the user application 134 may concurrently present information about the second merchant 604 having items 612 suitable for bundling with the items 610 of the selected first merchant 602” and “when bundled with items 610 from the first merchant 602”; also see FIG. 6);
generating, using the plurality of orders, a user interface signal (Wang, see: paragraph [0097] disclosing “generate the user interface” and see: paragraph [0098] disclosing “user application 134 may receive and present the information”); and
sending, via a network, the user interface signal to a device associated with the user, wherein the sending causes the device associated with the user to display a user interface with information about the plurality of orders and the plurality of sources for servicing the plurality of orders (Wang, see: paragraph [0101] disclosing “user interface 700 that may be presented on the display 137 of the user device 108” and “the user interface 700 includes an indication of the first merchant 702, and items 704 selected by the user from the first merchant 702 and corresponding prices. In addition, the user interface 700 presents a selected second merchant 706, i.e., BOB'S Convenience Store, along with a message 708 that the items in the item categories 710 from the second merchant 706 may be bundled with the selected items 704 from the first merchant 702”; and see: FIG. 7).
Although Wang does disclose generating for each of the items in the aggregated list, item information, Wang does not disclose:
an embedding vector of a plurality of embedding vectors, wherein the embedding vector is generated by:
using the data of each of the items with corresponding other item into a corresponding dimension of the embedding vector thereby reducing a first dimensionality of the cooccurrence data to a second dimensionality of the embedding vector,
using the plurality of embedding vectors for the items,
Maschmeyer, does teach:
an embedding vector of a plurality of embedding vectors, wherein the embedding vector is generated by (Maschmeyer, see: paragraph [0078] teaching “learned representation of discrete variables as vectors of numeric values, where the ‘dimension’ of the embedding represents the length of the vector” and paragraph [0081] teaching “a selection of an item” and “embeddings may represent a mapping between discrete variables and a vector…effectively capture meaning and/or relationships in the data”):
using the data of each of the items with corresponding other item into a corresponding dimension of the embedding vector thereby reducing a first dimensionality of the cooccurrence data to a second dimensionality of the embedding vector (Maschmeyer, see: paragraph [0082] teaching “a measure of similarity between each n-dimensional embedding may be represented by distance”; and paragraph [0083] teaching “a dimension reduction operation may be applied to the set of n-dimensional embeddings”; and see: paragraph [0089] teaching “quantization block 470 may apply a compacting algorithm to the set of lower dimensional embeddings 450” and “may represent items having a lower measure of similarity to the centroid object”),
using the plurality of embedding vectors for the items (Maschmeyer, see: paragraph [0104] teaching “a group of items representative of a cluster of similar items, for example being separated by a distance…in the n-dimensional embedding space on in the lower dimensional embedding space”).
This step of Maschmeyer is applicable to the method of Wang, as they both share characteristics and capabilities, namely, they are directed to user interactions with products on e-commerce websites. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Wang to include the features of an embedding vector of a plurality of embedding vectors, wherein the embedding vector is generated by: using the data of each of the items with corresponding other item into a corresponding dimension of the embedding vector thereby reducing a first dimensionality of the cooccurrence data to a second dimensionality of the embedding vector, and using the plurality of embedding vectors for the items, as taught by Maschmeyer. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Wang to improve ways that customers interact with products on an a commerce website, by determining similarities between products, allowing for increased transactions with merchants (Maschmeyer, paragraph [0006]).
Claim 2-
Wang in view of Maschmeyer teach the method of claim 1, as described above.
Wang discloses wherein the clustering comprises:
items in the aggregated list (Wang, see: paragraph [0096] disclosing “present items offered by a selected first merchant as indicated at 602 as well as items offered by a second merchant, as indicated at 604, that are available for bundling with items ordered from the first merchant”);
clustering, a corresponding subset of items in the aggregated list (Wang, see: FIG. 6 rendering the interface for selecting a list of items that are aggregated, where each of the clustered plurality of items are offered by different merchants, such as The Burger Store and Bob’s Convenience Store.);
the corresponding subset of items for the corresponding cluster (Wang, see: paragraph [0097] disclosing “generate the user interface 600, when the user 110 selects the first merchant 602 (i.e., THE BURGER STORE in this example) for…items 610 offered by the first merchant 602, the user application 134 may concurrently present information about the second merchant 604 having items 612 suitable for bundling with the items 610 of the selected first merchant 602” and “when bundled with items 610 from the first merchant 602”);
the corresponding cluster for each item in the aggregated list (Wang, see: paragraph [0097] disclosing “generate the user interface 600, when the user 110 selects the first merchant 602 (i.e., THE BURGER STORE in this example) for…items 610 offered by the first merchant 602, the user application 134 may concurrently present information about the second merchant 604 having items 612 suitable for bundling with the items 610 of the selected first merchant 602” and “when bundled with items 610 from the first merchant 602”);
wherein all items in each of the plurality of clusters are serviced by a different source (Wang, see: paragraph [0097] disclosing “generate the user interface 600, when the user 110 selects the first merchant 602 (i.e., THE BURGER STORE in this example) for…items 610 offered by the first merchant 602, the user application 134 may concurrently present information about the second merchant 604 having items 612 suitable for bundling with the items 610 of the selected first merchant 602” and “when bundled with items 610 from the first merchant 602”).
However, Wang does not disclose:
generating a respective distance of a plurality of distances between each pair of embedding vectors for each pair of items;
using the plurality of distances, items corresponding distances of the plurality of distances below a threshold distance into a corresponding cluster of the plurality of clusters;
recomputing, using embedding vectors of the corresponding subset of items, an embedding vector for the cluster;
recalculating a distance between the embedding vector for the corresponding cluster and at least one of an embedding vector for each item that is not in the plurality of clusters; and
repeating the clustering, the recomputing, and the recalculating until all items from the aggregated list are clustered into the plurality of clusters.
Maschmeyer, however, does teach:
generating a respective distance of a plurality of distances between each pair of embedding vectors for each pair of items (Maschmeyer, see: paragraph [0089] teaching “generate the quantized grid 480 as a compacted configuration of lower dimensional embeddings” and “may be focused around the centroid object 460, where spaced items arranged adjacent to or in close proximity to the centroid object 460 may represent items having a higher measure of similarity to the centroid object”);
using the plurality of distances, items corresponding distances of the plurality of distances below a threshold distance into a corresponding cluster of the plurality of clusters (Maschmeyer, see: paragraph [0089] teaching “where spaced items arranged far from the centroid object 460 may represent items having a lower measure of similarity to the centroid object 460”; and paragraph [0104] teaching “a group of items representative of a cluster of similar items, for example, being separated by a distance (e.g., a Euclidean distance) in the n-dimensional embedding space or in the lower dimensional embedding space, that is lower than a threshold distance, may be collapsed into a subset of items.”);
recomputing, using embedding vectors of the corresponding subset of items, an embedding vector for the cluster (Maschmeyer, see: paragraph [0099] teaching “engaging with a ‘recalculate’ element 570 after selecting a desired dimensionality reduction operation from the controls 560”);
recalculating a distance between the embedding vector for the corresponding cluster and at least one of an embedding vector for each item that is not in the plurality of clusters (Maschmeyer, see: paragraph [0099] teaching “engaging with a ‘recalculate’ element 570 after selecting a desired dimensionality reduction operation from the controls 560, may cause the recommendation engine 400 to reapply the dimensionality reduction to the n-dimensional set of embeddings”); and
repeating the clustering, the recomputing, and the recalculating until all items from the aggregated list are clustered into the plurality of clusters (Maschmeyer, see: paragraph [0099] teaching “recommendation engine 400 to reapply the dimensionality reduction to the n-dimensional set of embeddings, according to the specific dimensionality reduction operation, to generate a new lower dimensional map”).
This step of Maschmeyer is applicable to the method of Wang, as they both share characteristics and capabilities, namely, they are directed to user interactions with products on e-commerce websites. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Wang to include the features of generating a respective distance of a plurality of distances between each pair of embedding vectors for each pair of items; using the plurality of distances, items corresponding distances of the plurality of distances below a threshold distance into a corresponding cluster of the plurality of clusters; recomputing, using embedding vectors of the corresponding subset of items, an embedding vector for the cluster; recalculating a distance between the embedding vector for the corresponding cluster and at least one of an embedding vector for each item that is not in the plurality of clusters; and repeating the clustering, the recomputing, and the recalculating until all items from the aggregated list are clustered into the plurality of clusters, as taught by Maschmeyer. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify the reference of Wang to improve ways that customers interact with products on an a commerce website, by determining similarities between products, allowing for increased transactions with merchants (Maschmeyer, paragraph [0006]).
Claim 12-
Wang in view of Maschmeyer teach the method of claim 1, as described above.
Wang further discloses wherein sending the user interface signal comprises:
sending the user interface signal to the device associated with the user, wherein the sending causes the device associated with the user to display the user interface further with a plurality of user interface elements, each of the plurality of user interface elements having a functionality to confirm servicing of a respective order of the plurality of orders or to modify servicing of the respective order (Wang, see: paragraph [0101] disclosing “user interface 700 that may be presented on the display 137 of the user device 108” and “the user interface 700 includes an indication of the first merchant 702, and items 704 selected by the user from the first merchant 702 and corresponding prices. In addition, the user interface 700 presents a selected second merchant 706, i.e., BOB'S Convenience Store, along with a message 708 that the items in the item categories 710 from the second merchant 706 may be bundled with the selected items 704 from the first merchant 702”; and see: FIG. 7 rendering the user interface elements allowing the user to add specific items from the specific merchant, and also allow the user to choose delivery or pick up for the items.).
Claim 13-
Wang in view of Maschmeyer teach the method of claim 1, as described above.
Wang discloses wherein generating the plurality of orders comprises:
retrieving, from the database and using the identity of the user, the order data with information about a collection of orders placed by the user that were serviced by a collection of sources (Wang, see: paragraph [0021] disclosing “merchant(s) may be selected for the subset based on a variety of considerations” and “purchase history of the user, purchase histories of other users who purchased similar items…prior agreements between the service and one or more of the second merchants”); and
assigning, using the order data, each cluster of the plurality of clusters to a corresponding source of the plurality of sources, the corresponding source being unique for each cluster of the plurality of clusters (Wang, see: paragraph [0101] disclosing “user interface 700 that may be presented on the display 137 of the user device 108” and “the user interface 700 includes an indication of the first merchant 702, and items 704 selected by the user from the first merchant 702 and corresponding prices. In addition, the user interface 700 presents a selected second merchant 706, i.e., BOB'S Convenience Store, along with a message 708 that the items in the item categories 710 from the second merchant 706 may be bundled with the selected items 704 from the first merchant 702”; and see: FIG. 7.).
Claim 14-
Wang in view of Maschmeyer teach the method of claim 1, as described above.
Wang discloses wherein:
generating the user interface signal comprises generating the user interface signal including information about the corresponding source for servicing each order of the plurality of orders (Wang, see: paragraph [0101] disclosing “user interface 700 that may be presented on the display 137 of the user device 108” and “the user interface 700 includes an indication of the first merchant 702, and items 704 selected by the user from the first merchant 702 and corresponding prices. In addition, the user interface 700 presents a selected second merchant 706, i.e., BOB'S Convenience Store, along with a message 708 that the items in the item categories 710 from the second merchant 706 may be bundled with the selected items 704 from the first merchant 702”; and see: FIG. 7); and
sending the user interface signal comprises sending the user interface signal to the device associated with the user, wherein the sending causes the device associated with the user to display the user interface with the information about the corresponding source for servicing each order of the plurality of orders and a plurality of user interface elements, each user interface element of the plurality of user interface elements having a functionality to confirm servicing each order from the plurality of orders using the corresponding source, or to select a different source of the plurality of sources for servicing each order from the plurality of orders (Wang, see: paragraph [0101] disclosing “user interface 700 that may be presented on the display 137 of the user device 108” and “the user interface 700 includes an indication of the first merchant 702, and items 704 selected by the user from the first merchant 702 and corresponding prices. In addition, the user interface 700 presents a selected second merchant 706, i.e., BOB'S Convenience Store, along with a message 708 that the items in the item categories 710 from the second merchant 706 may be bundled with the selected items 704 from the first merchant 702”; and see: FIG. 7).
Regarding claim 15, claim 15 is directed to a computer program product. Claim 15 recites limitations that are similar in nature to those addressed above for claim 1, which is directed towards a method. It is additionally noted that claim 15 recites the features of a computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps (Wang, claim 15, disclosing “One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, configure the one or more processors to perform operations”). Claim 15 is therefore rejected for the same reasons as set forth above for claim 1.
Regarding claim 16, claim 16 is directed to a computer program product. Claim 16 recites limitations that are parallel in nature to those addressed above for claim 2, which is directed towards a method. Claim 16 is therefore rejected for the same reasons as set forth above for claim 2.
Regarding claim 19, claim 19 is directed to a computer program product. Claim 19 recites limitations that are parallel in nature to those addressed above for claim 14, which is directed towards a method. Claim 19 is therefore rejected for the same reasons as set forth above for claim 14.
Regarding claim 20, claim 20 is directed to a system. Claim 20 recites limitations that are similar in nature to those addressed above for claim 1, which is directed towards a method. It is additionally noted that claim 20 recites the features of a computer system comprising: a processor; and a non-transitory computer-readable storage medium having instructions that, when executed by the processor, cause the computer system to perform steps (Wang, claim 15, disclosing “One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, configure the one or more processors to perform operations”). Claim 20 is therefore rejected for the same reasons as set forth above for claim 1.
Reasons for Allowable Subject Matter
Prior Art Considerations:
Upon review of the evidence at hand, it is concluded that the totality of evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
Regarding the dependent claims 3 and 17, the features are as follows:
applying the clustering machine-learning model to the embedding vector of each item from the aggregated list to generate a plurality of scores for each item from the aggregated list, each score of the plurality of scores indicating a likelihood that each item from the aggregated list belongs to a respective cluster of the plurality of clusters; and
grouping, using the plurality of scores for each item, each item from the aggregated list to a corresponding cluster of the plurality of clusters
The most apposite prior art of record includes Wang, G., et al. (PGP No. US 2022/0101250 A1), in view of Maschmeyer, R., et al., (PGP No. US 2025/0165125 A1), and Laserson, I., et al. (PGP No. US 2022/0092670 A1), to teach a method for bundling orders from different merchants.
The reference of Wang describes a system for bundling items into orders from different merchants, where the system can receive an order from a user device, where the request for an order includes orders for multiple items (Wang, paragraphs [0029], [0033], and [0037]-[0038]). The system determines from the requested order, if the items are currently in the inventory from a specific merchant catalogue, where the system will indicate if the items in the list are not currently available for the order (Wang, paragraph [0039]). The system of Wang further utilizes past order information to bundle combinations of items that are frequently bundled together (Wang, paragraphs [0051], [0060], and [0104]). The bundling of Wang causes the initial display of the items, such as the frequently ordered together bundles determined from historical orders, where the items offered can be offered from more than one merchant, as in this case, a first and a second merchant (Wang, see: paragraph [0096] and FIG. 6). Orders are then created and displayed via the user interface for the selected first and second merchants, bundling the items concurrently visible within the same user interface (Wang, paragraphs [0097]-[0098] & [0101], and FIG. 6 and FIG. 7). Although Wang describes these features of bundling items into orders from different merchants, Wang does not describe any type of embedding vectors, nor does the reference discuss scores that are used for indicating a likelihood that each item from the aggregated list belongs to a respective cluster of the plurality of clusters.
The reference of Maschmeyer does teach the features of vectors with numeric values, vector embeddings are represented by a specific length of the vector, and where the embedding is represented via a visible mapping of the variables and vectors, rendering the relationships between specific items (Maschmeyer, paragraphs [0078] and [0081]). The reference measures the similarities between each embedding that represents the distances, and further explains that a dimension reduction operation can be utilized to the set of embeddings, where an algorithm is applied to lower the dimensional embeddings, representing items that have a lower calculated similarity to a central item (Maschmeyer, paragraphs [0083]-[0083], [0089], and [0104]). Although Maschmeyer does describe the vector embeddings, representing relationships and distances between items in a mapping space, Maschmeyer does not describe applying the clustering machine-learning model to the embedding vector of each item from the aggregated list to generate a plurality of scores for each item from the aggregated list, each score of the plurality of scores indicating a likelihood that each item from the aggregated list belongs to a respective cluster of the plurality of clusters, and grouping, using the plurality of scores for each item, each item from the aggregated list to a corresponding cluster of the plurality of clusters.
The reference of Laserson is relied upon to demonstrate mapping similarities between items via item codes within a multidimensional space (Laserson, paragraph [0012]), where transaction histories are utilized to create item code vectors for items, and where the item code vectors are then input into the mapping manager (Laserson, paragraph [0029]). Further, the reference describes that the output via a Word2Vec algorithm, is an item replacement code for out of stock items, along with a similarity score that has been generated based on distances within the multidimensional space calculated (Laserson, paragraph [0029]). Laserson further explains that the similarity scores calculated from the distances in the multidimensional space, are used to determine recommendations for substitute item codes, where the score in that case would be preset threshold value of a predefined top number of similarity scores (Laserson, paragraph [0029]). Although Laserson does describe similarity scores based on a calculated distance within a multidimensional space, Laserson does not describe that the scores indicate any type of likelihood that the items from a list belong to a specific cluster of a plurality of clusters, nor that the score for each item corresponds to clusters of the plurality of clusters.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. Moreover, the combination of features of dependent claims 3 and 17, would not have been obvious to one of ordinary skill in the art because any combination of evidence at hand to reach the combination of features as claimed would require substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias and resulting in an inappropriate combination. Claims 4-11 and claim 18, depending from claims 3 and 17 respectively, also recite allowable subject matter based on their dependencies.
It is hereby asserted by the Examiner, that in light of the above and in further deliberation over all of the evidence at hand, that the dependent claims recite allowable subject matter as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Latzina, M. (PGP No. US 2018/0268082 A1), describes a processor executing a clustering application having an interactive user-interface rendered on a client computer. The clustering application determines a first cluster of data items of a data set, the data items in the first cluster having first attribute values that are similar to each other within a first degree of similarity and determines a second cluster of data items of the data set, the data items in the second cluster having second attribute values that are similar to each other within a second degree of similarity.
Non-patent literature (NPL) document, Mi9 Retail Suite, published on mi9retail.com (2018), describes retail merchandising for retailers formatted for different retail segments, such as online retail, brick and mortar, wholesale retail, warehouse retailer, etc., where the system for retailers is used to provide accurate retail data and analytical solutions in real-time.
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688
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