CTNF 18/360,155 CTNF 100042 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Response to Amendment The Amendment filed March 12 th , 2026 has been entered. Claims 1-20 are pending in the application. Applicant’s amendment to Claim 17 has overcome the rejection previously set forth in the Non-Final Office Action mailed December 12 th , 2025. A second search has been performed to address the material amended in the aforementioned claims. Newly found reference Powers (US 20220122031 A1) was used for the amended claim limitations. Response to Arguments Applicant’s arguments, filed March 12th 2026, with respect to claims 1, 6, 8, 12, 17, and 18 have been fully considered. Responses to the arguments are listed below. The arguments for claims 6, 8, and 12 were found persuasive, the arguments for claims 1 and 18 were not found persuasive, and the arguments for claim 17 were moot. Regarding claim 1: The applicant argues that: “A projection surface used to display an image of an article is not the same as the claimed "functional space of the user associated to the article." (Pg. 7) and that “determining whether a projected representation fits on or at a projection surface is not the same as assessing whether the article fits in a functional space of the user as recited.” (Pg. 7) The Examiner submits that the broadest reasonable interpretation of the term “functional space” includes the wall or user body location taught by Hazlewood, and that there is no recitation in the claims (prior to amendment) that limits the term further. The Applicant argues that “although it is impermissible to read limitations from the specification into the claims, a formulated construction under the broadest reasonable interpretation standard must still be reasonable in light of the specification.” (Pg. 8) The specification of the present application teaches more details about assessing fitting of an article in a functional space identified for the article. Specifically, it states that: “Manager system 110 running assessing process 117 can compare dimensions of an article for acquisition to dimensions of open areas within functional spaces identified for the article for acquisition.” [0038]. It also teaches that “functional spaces” may be everyday locations: “Embodiments herein recognize that respective users of system 100 can include associated functional spaces, e.g., functional spaces of a residence or office of a user, functional spaces of an operations venue. In one example, a residence of a user can be divided, e.g., into a kitchen functional space, a home gym functional space, a living room functional space , a garage functional space, a home office functional space, and so on.” [0026] (emphasis added). Hazlewood teaches that: “it may not be readily apparent to the user from any images of the vase available from the electronic marketplace, how the vase would look or fit in a space of the user's living room in which the user is interested in placing the vase […] the user may project a representation of the vase onto a projection surface located in the space to help the user visualize how the vase will look or fit in that space .” (paragraph (21), emphasis added). In the example, Hazlewood describes that a user can assess the fit of the vase in the living room functional space. Therefore, the Examiner believes it would be reasonable for one of ordinary skill in the art to read Hazlewood as teaching assessing whether the article fits in a functional space as claimed. For at least the above reasons, the Examiner respectfully disagrees that Hazlewood “does not disclose ‘in response to the determining that the user has selected the article for acquisition, identifying a functional space of the user associated to the article’ as recited”. Accordingly, the §102 rejection of claim 1 over Hazlewood is maintained. Regarding claim 6: The arguments regarding claim 6 are persuasive, and therefore the corresponding rejection is withdrawn. However, upon further consideration, a new rejection was made in view of Powers (US 20220122031 A1) . Regarding claim 8: The Applicant argues that: “Hazlewood, on current review, still does not disclose identifying from such processing that the article is operationally larger than a recorded dimension of the article in the manner recited”. The Examiner respectfully disagrees, because Hazlewood teaches using an identification of an item larger than a recorded dimension as criteria for a user search: “The electronic marketplace module 242 may also be configured to allow a user to search for an item with particular restrictions (e.g., physical dimensions). For example, the user may provide rules or criteria (e.g., identified by the user or by dimensional analysis of an item), other recommendations or feedback (e.g., other users claim that a 60-inch television is too big for a 90-inch space), budget, or other restrictions.” (paragraph 77). The Applicant also argues that: “Hazlewood's cited feedback about an item being too big for a space is not the same as identifying, based on natural language processing, that the article is operationally larger than a recorded dimension of the article.” The Examiner respectfully disagrees, because, as in the example, if a supposed 60-inch television is “too big” for a 90-inch space, it must be the case that the 60-inch television is “operationally larger” than 60 inches. The Examiner also notes that while the terminology differs between Hazlewood and the claim language, the terms do not need to be identical: “The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).” (see MPEP 2131). However, the other arguments regarding Pieper are persuasive, and therefore the corresponding rejection is withdrawn. However, upon further consideration, a new rejection was made in view of Siddiqui (US 20180276726 A1). Regarding claim 12: The arguments regarding claim 12 are persuasive, and therefore the corresponding rejection is withdrawn. However, upon further consideration, a new rejection was made in view of Assouline (US 20220327608 A1). Regarding claim 17: Applicant’s arguments with respect to claim 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Newly found reference Powers (US 20220122031 A1) was used for the amended limitations. Regarding claim 18: The Applicant argues that: “Nor does that disclosure teach or suggest identifying a user-associated three-dimensional interior space having walls and being associated with a room, office, or domicile of the user as now expressly recited in claim 18.” The Examiner respectfully disagrees, because Hazlewood teaches that the projection surface may be associated with a living room, as discussed in the response to the argument of claim 1 above. In the previous interview, the Applicant noted the length of the Non-Final action. In response, the Examiner has attempted to consolidate the content of the action while still maintaining an appropriate level of detail. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1, 3, and 18 are rejected under 35 U.S.C 102(a)(2) as being anticipated by Hazlewood (US 9715865 B1; see attached document for paragraph numbers) . Regarding claim 1: Hazlewood teaches: A computer implemented method (Hazlewood: non-transitory computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data (62)) comprising: determining that a user has selected an article for acquisition (Hazlewood: a user can operate a projection equipped computing device to order a pair of high-heeled shoes, hockey gear, or jewelry (e.g., “wearable items”) from an electronic marketplace. Before placing an order for a wearable item, the user can select an option from a network page generated by the electronic marketplace and display the item to “try on the item.” (24)) ; in response to the determining that the user has selected the article for acquisition, identifying a functional space of the user associated to the article (Hazlewood: The projection equipped computing device may also measure a distance between a location of the projection equipped computing device and a projection surface (e.g., a user's neck where they will wear the necklace, or a wall where the height of the high heels will be projected, etc.). (24)) ; in response to the identifying the functional space of the user associated to the article, assessing fitting of the article in the functional space of the user (Hazlewood: the analysis of the light beam may determine if the representation of the item fits at the projection surface (103)) ; and interacting with the user in dependence on the assessing fitting of the article in the functional space of the user (Hazlewood: The projection module 240 may also be configured to generate a notification. […] In some examples, the notification can identify whether the item will fit in a space associated with the projection surface. (76)) , wherein the interacting with the user includes adaptively (see Note 1A ) generating user interface prompting data in dependence on the assessing fitting of the article in the functional space of the user (Hazlewood: the notification may also or alternatively include information as to whether the item fits in the desired space (e.g., “Congratulations! You selected an item that will fit in the space associated with the projection surface.”) (104)) , and presenting the adaptively generated user interface prompting data to the user (Hazlewood: FIG. 11 is a pictorial diagram illustrating an example notification 1130 that may be presented to a user of the projection equipped computing device 1100 (102)) . Note 1A : The Examiner notes the use of the term “adaptively”. Because Hazlewood teaches that the notification may be adapted based on whether the item fits the space or not, the notification may reasonably be considered “adaptively generated”. Regarding claim 3: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), wherein method includes finding by the assessing fitting that the article is qualified for fitting in the functional space (Hazlewood: the analysis of the light beam may determine if the representation of the item fits at the projection surface (paragraph 103)) , and wherein the presenting prompting data includes presenting prompting data that specifies to the user that the article is qualified for fitting in the functional space (Hazlewood: the notification may also or alternatively include information as to whether the item fits in the desired space (e.g., “Congratulations! You selected an item that will fit in the space associated with the projection surface.”) (paragraph 104)) . Regarding claim 18: Claim 18 is substantially similar to claim 1 (see comparison below), and is therefore rejected for similar reasons. Claim 18 contains the following notable differences: Claim 18 claims a system instead of a computer implemented method. Hazlewood teaches a system: A system comprising: a memory; at least one processor in communication with the memory; and program instructions executable by one or more processor via the memory to perform a method (Hazlewood: non-transitory computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data (paragraph 62)) Claim 18 recites: wherein the functional space comprises a user-associated three-dimensional interior space having walls and being associated with a room, office, or domicile of the user; Hazlewood teaches: “The space may correspond with a projection surface identified by the user” (paragraph 91). Hazlewood also teaches that the space may be a user-associated living room: “it may not be readily apparent to the user from any images of the vase available from the electronic marketplace, how the vase would look or fit in a space of the user's living room” (paragraph 21, emphasis added). The projection surface may be a wall: “For example, the projection surface can include a wall, corner, top of a cabinet, desk, table, body part of a user, or any other surface where an actual item may be placed or viewed (e.g., after the item is ordered and received or upon request).” (paragraph 29). In claim 1, the Examiner analogized the functional space to the projection surface, which, as shown above, includes a user-associated wall which may be associated with a three-dimensional interior room (the living room). Therefore, the Examiner understands Hazlewood to teach the amended claim 18. PNG media_image1.png 306 414 media_image1.png Greyscale Differences between claim 1 and claim 18 . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 2, 4, 5, and 7 are rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Pieper (US 20080163054 A1) . Regarding claim 2: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space. Pieper teaches: wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space (Pieper: if the simulation generated at step 420 and analyzed at step 425 determines that the consumer has selected a personal care product that is too small (which would be unconformable) or too large (which may be prone to leak) based on the avatar representation of the individual, then alternatives sizes, products, or product configurations may be recommended, [0059]; see Note 2A ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Pieper, would benefit the Hazlewood teachings by enabling the user to more easily locate an item that does fit after picking an item that did not fit. Note 2A : Unlike Hazlewood, Pieper is more directed to fitting of clothing on avatars as opposed to fitting a packaged product in a region of 3D space. However, the Examiner submits that the differences between the two methods amount to what products they are applied to and that one of ordinary skill in the art would be able to apply the methods of Pieper on the products in Hazlewood and vice versa. Regarding claim 5: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, wherein the presenting prompting data includes presenting prompting data that specifies the one or more alternate article. Pieper teaches: wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space (Pieper: if the simulation generated at step 420 and analyzed at step 425 determines that the consumer has selected a personal care product that is too small (which would be unconformable) or too large (which may be prone to leak) based on the avatar representation of the individual, then alternatives sizes, products, or product configurations may be recommended, [0059]) , wherein the presenting prompting data includes presenting prompting data that specifies the one or more alternate article (Pieper: In such a case, at step 440, the consumer may select to review a simulation of the recommended changes, and the method 400 may return to step 410 and generate a simulation of the modified product interacting with the avatar representation of the individual [0059]; see Note 5A ) . Note 5A : Pieper teaches that the consumer may review a simulation of the recommended item, which requires that data specifying the recommended item is presented to the consumer. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Pieper, would benefit the Hazlewood teachings by enabling the user to more easily locate an item that does fit after picking an item that did not fit. Regarding claim 7: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein the assessing fitting includes disqualifying the article on the determining that the article is too large to be fitted in the functional space. Pieper teaches: wherein the assessing fitting includes disqualifying the article on the determining that the article is too large to be fitted in the functional space (Pieper: if the simulation generated at step 420 and analyzed at step 425 determines that the consumer has selected a personal care product that is […] too large (which may be prone to leak) based on the avatar representation of the individual, then alternatives sizes, products, or product configurations may be recommended, [0059]) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. disqualifying the article on the determining that the article is too large to be fitted in the functional space, as in Pieper, would benefit the Hazlewood teachings by ensuring that a chosen item is not too large to fit in the space the user has available . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) and Assouline (US 20220327608 A1) . Regarding claim 4: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space. Assouline teaches: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article (Assouline: For example, the expected object module 516 can determine that the room classification is a living room and that a sofa, coffee table, and rug furniture items are missing from the list of objects provided by the object detection module 512 or room classification module 514. In such cases, the expected object module 516 can determine that the sofa and coffee table furniture items have higher scores than the rug furniture item and can in response select only the sofa and coffee table as furniture items to recommend to a user to purchase for inclusion in the room [0109]; see Note 4A ) , and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space (Assouline: The expected object module 516 can also process the 3D mesh representation of the room to compute an amount of available physical space remaining in the room depicted in the image. In response, the expected object module 516 can determine that only the coffee table furniture item can physically fit within the dimensions of the room and that the room cannot physically fit all three missing furniture items. [0111]; see Note 4B ) . Note 4A : The specification of the present application recites that “As set forth in reference to article predicting process 115 described in reference to Fig. 1, manager system 110 can ascertain that the user will acquire a complementary article when selecting an article for acquisition.” [0100]. Therefore, the ascertaining is understood to be a prediction (e.g. suggesting related items) rather than knowledge that the user has decided to select a complementary item (e.g. the user has selected a “Find similar” option). Note 4B: In other words, Assouline may recommend items that are missing from the room based on the items currently present in the room. Assouline may also check whether the recommended items will fit in the space with the items currently present in the room. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Assouline with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Assouline, would benefit the Hazlewood teachings by helping the user select items that will fit in a space more efficiently: “Typically, virtual reality (VR) and augmented reality (AR) systems allow users to add augmented reality elements to their environment […] the user of these systems has to spend a great deal of effort searching through and navigating multiple user interfaces and pages of information to identify an item of interest. Then the user has to manually position the selected item within view. These tasks can be daunting and time consuming, which detracts from the overall interest of using these systems and results in wasted resources” (Assouline, [0014]) . 07-21-aia AIA Claim s 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) and Powers (US 20220122031 A1) . Regarding claim 6: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein the assessing fitting includes disqualifying the article on the determining that the inclusion of the article in the functional space will leave a threshold satisfying amount of additional area in the functional space. Powers teaches: wherein the assessing fitting includes disqualifying the article on the determining that the inclusion of the article in the functional space will leave a threshold satisfying amount of additional area in the functional space (Powers: A first item is placed in a first container, and a volume remaining is determined. A second item is then evaluated to see if it will fit in the first container; if so, it is placed in the first container and a new volume remaining is determined. If not, the second item is placed in a newly-opened second container. [0054]; Powers: A container may be invalid when it has insufficient weight or volume remaining [0055]; see Note 6A ) . Note 6A : Powers teaches: “A first item is placed in a first container, and a volume remaining is determined. A second item is then evaluated to see if it will fit in the first container; if so, it is placed in the first container and a new volume remaining is determined. If not, the second item is placed in a newly-opened second container.” [0054]. In other words, the item is disqualified from the first container if the volume remaining within the first container is less than a threshold “insufficient” amount. Similarly, the specification of the present application teaches that an article is disqualified if the space than can be utilized by an item is exceeded: “In other words, manager system 110 can be configured to disqualify an article when manager system 110 detects that there is a threshold exceeding the amount of additional space that can be utilized by a selected article for acquisition.” [0073]. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Powers with Hazlewood. Disqualifying the article on the determining that the inclusion of the article in the functional space will leave a threshold satisfying amount of additional area in the functional space, as in Powers, would benefit the Hazlewood teachings by enabling more efficient packing: “previous packing methods typically attempt to optimize volume utilization or other dimensional values. These methods fail to achieve a cost-optimal solution because they fail to consider a wide range of cost factors beyond simply the dimensions of the items to be shipped” (Powers, [0003]). Regarding claim 17: Hazlewood teaches: A computer program product comprising: a computer readable storage medium readable by one or more processing circuit and storing instructions for execution by one or more processor for performing a method (Hazlewood: non-transitory computer-readable storage media may include volatile or non-volatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data (62)) comprising: determining that a user has selected an article for acquisition (Hazlewood: a user can operate a projection equipped computing device to order a pair of high-heeled shoes, hockey gear, or jewelry (e.g., “wearable items”) from an electronic marketplace. Before placing an order for a wearable item, the user can select an option from a network page generated by the electronic marketplace and display the item to “try on the item.” (24)) ; in response to the determining that the user has selected the article for acquisition, identifying a functional space of the user associated to the article (Hazlewood: The projection equipped computing device may also measure a distance between a location of the projection equipped computing device and a projection surface (e.g., a user's neck where they will wear the necklace, or a wall where the height of the high heels will be projected, etc.). (24)) ; in response to the identifying the functional space of the user associated to the article, assessing fitting of the article in the functional space of the user (Hazlewood: the analysis of the light beam may determine if the representation of the item fits at the projection surface (103)) ; and interacting with the user in dependence on the assessing fitting of the article in the functional space of the user (Hazlewood: The projection module 240 may also be configured to generate a notification. […] In some examples, the notification can identify whether the item will fit in a space associated with the projection surface. (76)) , wherein the interacting with the user includes adaptively (see Note 1A ) generating user interface prompting data in dependence on the assessing fitting of the article in the functional space of the user (Hazlewood: the notification may also or alternatively include information as to whether the item fits in the desired space (e.g., “Congratulations! You selected an item that will fit in the space associated with the projection surface.”) (104)) , and presenting the adaptively generated user interface prompting data to the user (Hazlewood: FIG. 11 is a pictorial diagram illustrating an example notification 1130 that may be presented to a user of the projection equipped computing device 1100 (102)) . wherein the adaptively generating includes generating user interface prompting data that specifies a fit status of the article relative to the functional space of the user based on the assessing fitting (Hazlewood: the notification may also or alternatively include information as to whether the item fits in the desired space (e.g., “Congratulations! You selected an item that will fit in the space associated with the projection surface.”) (104)) . Hazlewood fails to teach: identifying a functional space of the user associated to the article, wherein the functional space comprises a user-associated three-dimensional space having bounded spatial extent for placement of the article; wherein the assessing fitting includes evaluating dimensional data of the article relative to dimensional data of the functional space to determine whether inclusion of the article in the functional space satisfies a fit condition; Powers teaches: identifying a functional space of the user (Powers: obtaining a request for a packing instruction from a user via a user interface [0006]) associated to the article (Powers: the packing instruction comprises a machine-readable result of the selected layout configuration, which may comprise information related to: […] coordinates of every item packed within the container, [0006]) , wherein the functional space comprises a user-associated three-dimensional space having bounded spatial extent for placement of the article (Powers: the request comprises: weight information and dimension information of the plurality of products, dimension information of one or more containers available for containing the plurality of products; [0006]; see Note 17A ) ; wherein the assessing fitting includes evaluating dimensional data of the article (Powers: Packing Computer 107 may be coupled to a scanner that scans an item to determine its dimensions, or a scale that weighs the item to determine its weight. Those dimensions may then be used in the request sent to the API Host Server 101 to establish an optimal packing solution. [0044]) relative to dimensional data of the functional space to determine whether inclusion of the article in the functional space satisfies a fit condition (Powers: A first item is placed in a first container, and a volume remaining is determined. A second item is then evaluated to see if it will fit in the first container; if so, it is placed in the first container and a new volume remaining is determined. If not, the second item is placed in a newly-opened second container. The process continues with placing subsequent items in an existing box or opening a new box. [0054]) ; Note 17A : Powers showcases in Fig. 5 that a container has a bounded spatial extent, and that: “GUI 500 also shows information of Container 1, which has a container type called Extra Large Box 2, a container weight of 17 lbs 6.4 oz, and a container dimension of 6 inches by 15.75 inches by 14.125 inches.” [0084]. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Powers with Hazlewood. Having the functional space comprise a user-associated three-dimensional space having bounded spatial extent for placement of the article and evaluating dimensional data of the article relative to dimensional data of the functional space to determine whether inclusion of the article in the functional space satisfies a fit condition, as in Powers, would benefit the Hazlewood teachings by enabling more efficient packing: “previous packing methods typically attempt to optimize volume utilization or other dimensional values. These methods fail to achieve a cost-optimal solution because they fail to consider a wide range of cost factors beyond simply the dimensions of the items to be shipped” (Powers, [0003]) . 07-21-aia AIA Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) and Siddiqui (US 20180276726 A1) . Regarding claim 8: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), wherein the method includes performing natural language processing of crowdsourced text (Hazlewood: other users claim that a 60-inch television is too big for a 90-inch space, (77); see Note 8A ) based content associated to the article, identifying based on the natural language processing that the article is operationally larger than a recorded dimension of the article (Hazlewood: The electronic marketplace module 242 may also be configured to allow a user to search for an item with particular restrictions (e.g., physical dimensions). For example, the user may provide rules or criteria (e.g., identified by the user or by dimensional analysis of an item), other recommendations or feedback (e.g., other users claim that a 60-inch television is too big for a 90-inch space), budget, or other restrictions (77); see Note 8B ) , and Note 8A : Parsing detailed user feedback as in the example in paragraph (77) of Hazlewood requires a form of natural language processing. Note 8B : Hazlewood teaches that the user feedback detailing that the 60-inch television is too large for the 90-inch space may be used as criteria for searching an item: “The electronic marketplace module 242 may also be configured to allow a user to search for an item with particular restrictions (e.g., physical dimensions).” (77). This requires identifying, via the feedback, that the item was operationally larger than a recorded dimension of the article. Hazlewood fails to teach: increasing the recorded size of the article for use in performing the assessing fitting responsively to the identifying based on the natural language processing that the article is operationally larger than a recorded dimension of the article. Siddiqui teaches: modifying recorded attributes of an article responsively to the identifying based on the natural language processing (Siddiqui: determining an accuracy score for existing product information using a first set of rules that compares the existing product information for the product with product information of other internal or external sources, determining if the accuracy score exceeds a predetermined accuracy threshold, automatically replacing incorrect product information in the existing product information with correct product information from the other sources, Abstract). increasing the recorded size of the article for use in performing the assessing fitting responsively to the identifying based on the natural language processing that the article is operationally larger than a recorded dimension of the article Siddiqui teaches: “Product attributes in the existing product information can comprise but are not limited to brand, color, product dimensions , etc.” [0045] (emphasis added) and that incorrect product information may be corrected based on “external product information of one or more external sources external to the online retailer.” [0016]. Hazlewood teaches determining that users external to the retailer may claim that an article has incorrect dimensions (i.e., dimensions of the actual TV are larger than the recorded dimensions) based on natural language processing of user feedback. When the teachings of Siddiqui are combined with Hazlewood, it would be obvious to increase the recorded size of the article for use in performing the assessing fitting responsively to the identifying based on the natural language processing that the article is operationally larger than a recorded dimension of the article. Similarly, the specification teaches that “In one example, where a threshold satisfying percentage of reviews are tagged with topics indicating that the article is functionally larger than expected, manager system 110 can increase a recorded dimension for the article, i.e., relative to recorded baseline dimensions for the article as determined from baseline text documents such as article specifications, instruction manuals, and the like.” [0096]. Therefore, the Examiner submits that Hazlewood in view of Siddiqui teaches the limitations of claim 8 . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Pieper (US 20080163054 A1) and Farshori (US 20190362381 A1; from applicant’s IDS) Regarding claim 9: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users, and articles data on multiple articles, wherein the method includes iteratively updating the user data, spaces data, and the articles data of the data repository, wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository. Pieper teaches: wherein the method includes prior to the determining, establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users (Pieper: receiving a selection of a population of avatars. Each avatar provides a representation of at least a portion of a body and the population of avatars is representative of a population of individuals [0015]) , and articles data on multiple articles (Pieper: The method also includes obtaining a set of data describing a product to be worn by the individuals in the population of individuals. [0014]) , Note 9D : As discussed in Note 2A, Pieper is focused more on fitting garments on an avatar than fitting packages into a space. In this case, one of ordinary skill in the art would consider the avatar body analogous to the space in which the fitting of the article is to be performed. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by enabling a user to ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Pieper still fails to teach: wherein the method includes iteratively updating the user data, spaces data, and the articles data of the data repository, wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository. Farshori teaches: wherein the method includes iteratively updating (Farshori: The server computer 112 can repeat one or more of the operations 202-208 repeatedly, if desired. [0073]) the user data, spaces data, and the articles data (see Note 9A ) of the data repository (Farshori: the shopping service 110 can be configured to store the shopping data 116 at a data store 118. [0050]; see Note 9A ) , wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository (Farshori: The use of this [compatibility] data is illustrated and described in more detail below, but briefly this information can be used, for example, to determine if a product being considered for purchase or being purchased can be transported home in a user's vehicle; if the product will fit in a particular home, office, or other location; or the like [0046]; see Note 9B ) . Note 9A: Farshori teaches that: “The shopping data can represent the user data, history information, the collected data, other data, and the like [0009]). The Examiner submits that the shopping data in Farshori encapsulates the claimed user data, spaces data, and articles data: In [0009] above, Farshori teaches that the shopping data may represent user data. Farshori teaches that: “In some embodiments, the user data can include compatibility data associated with a user. The compatibility data can identify dimensions of an entity associated with the user. [0018]. The compatibility data is understood to be analogous to the spaces data because Farshori further teaches: “the user or other entity can enter data relating to the user's car (e.g., a make, model, year, and/or trim level of the vehicle) at any time and/or as part of a setup process. The user or other entity also can enter the user's carrying weight maximum (e.g., the user may indicate that he or she is comfortable carrying up to thirty pounds, for example); dimensions of a room, home, office, or other space; body dimensions of the user; …” [0138]. Farshori further teaches: “the user data can include object data. The object data can identify an object on a shopping list”. The object data is understood to be analogous to the claimed articles data. All three data are stored in the user data, which is included within the shopping data. In [0050] cited above, the shopping data is stored in a data store. Farshori teaches that: “the server computer 112 can update, for example, the history information and/or collected data 120 components of the shopping data 116” [0079]. Therefore, Farshori teaches “iteratively updating the user data, spaces data, and the articles data of the data repository” [0079]. Note 9B : The compatibility data is part of the shopping data stored in the data store (as shown in Note 9A). When the compatibility data is stored in the data store, performing fitting based on said data requires obtaining it from the data store. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by ensuring the system has access to a central location where user and article data can be readily accessed . 07-21-aia AIA Claim s 10 and 20 are rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Delgado (US 20220130126 A1) . Regarding claim 10 : Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to explicitly teach: wherein the method includes determining that the user has selected a subsequent article for acquisition, in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user; and wherein the method includes in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article. Delgado teaches: wherein the method includes determining that the user has selected a subsequent article for acquisition (Delgado: the user 1250 of FIG. 11A is interested in determining whether a second box can fit within the trunk 1204 of the car 1202 together with the first box [0239]) , in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article (Delgado: the user device 1100 may detect surfaces 1212, 1214, 1216, 1218, 1220 and 1222 of the interior of the trunk 1204, surfaces 1226, 1228, 1230 and 1232 around the edge 1224 of the trunk 1204, [0229]; see Note 10A ) , wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user (Delgado: in order to facilitate an assessment of the fit of one or more virtual objects within an AR space a user may change the display settings (e.g. activate/deactivate the blend feature and/or change the opacity with which 3D representations of virtual objects are displayed) and/or navigate within the AR space in order to view the virtual object(s) from multiple perspectives [0248]; see Note 10B ) ; and wherein the method includes in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user (see Note 10B ), wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article (Delgado: the AR engine 322a may determine a 3D fit of the virtual object within the 3D bounded space by performing collision detection, which detects whether the virtual object fits within the 3D bounded space without colliding with any boundary of the 3D bounded space [0179]) , and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article (Delgado: each of the virtual objects is repositionable in the AR space 1210 independent of the other to allow assessment of the 3D fit of the virtual objects 1260 and 1290 together within the trunk 1204 in different positions [0242]; see Note 10C ) . Note 10A : Delgado teaches in [0229] above that the user device may detect the surfaces within and around the functional space. When the user wants to determine if a second box can fit within the trunk Note 10B : The Examiner notes that “determining that the user has selected the article for acquisition, identifying a functional space of the user associated to the article; in response to the identifying the functional space of the user associated to the article, assessing fitting of the article in the functional space of the user; and interacting with the user in dependence on the assessing fitting of the article in the functional space of the user” were taught by Hazlewood as shown in claim 1. It would be obvious to one of ordinary skill in the art to perform a similar method with a subsequent article. Hazlewood has taught (as shown in the mapping of claim 1) that the device may automatically perform assessment of the fit of a first article. In [0248] above, Delgado teaches that the user may perform assessment of the fit of one or more articles. It would be obvious to one of ordinary skill in the art to extend Hazlewood based on the teachings of Delgado to enable the user device to automatically perform assessment of the fit of one or more articles, and generating a notification or prompt based on the assessment of the fitting. Note 10C : Delgado teaches “assessing that the 3D representation of the virtual object 1260 fits within the trunk 1204” [0237]. That is, a “first space” with just the virtual object 1260 is assessed. Delgado further teaches: “In this example each of the virtual objects is repositionable in the AR space 1210 independent of the other to allow assessment of the 3D fit of the virtual objects 1260 and 1290 together within the trunk 1204 in different positions.” [0242]. That is, Delgado teaches that a “second space” with at least one other object may be assessed. The Examiner considers the example taught by Delgado to be analogous to the example to the content of paragraph [0117] of the specification of the present application, which states that: “manager system 110 can assess a first aggregate open space defined by open areas A, B, and C. However, where the functional space is transformed by inclusion of a described prompted for treadmill, […] manager system 110 can assess a transformed functional space now having a transformed aggregate open space …”. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Delgado with Hazlewood in view of Pieper. Assessing fitting the article in the functional space by evaluating a first and second aggregate open space of the functional space relative to article, as in Delgado, would benefit the Hazlewood in view of Pieper teachings by enabling a user to add multiple items to a scene and assess the fitting for each item individually. Regarding claim 20: Claim 20 is substantially similar to claim 10, and is therefore rejected for similar reasons. Claim 20 contains the following notable differences: Claim 20 claims a system instead of a computer implemented method. In the rejection of claim 18, it was shown that Hazlewood teaches a system . 07-21-aia AIA Claim 11 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1) in view of Pieper (US 20080163054 A1), Farshori (US 20190362381 A1; from applicant’s IDS) and Delgado (US 20220130126 A1) . The Examiner notes that claim 11 contains limitations also seen in claims 5, 9, and 10 (see text comparison below). For the content specific to claim 11, see the teachings of Delgado in this section. PNG media_image2.png 617 410 media_image2.png Greyscale PNG media_image3.png 620 415 media_image3.png Greyscale Comparison of claim 11 versus claim 9 on left, and versus claim 10 on the right. Regarding claim 11: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood alone fails to teach the remainder of the limitations of claim 11. Pieper teaches: wherein the method includes prior to the determining, establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users (Pieper: receiving a selection of a population of avatars. Each avatar provides a representation of at least a portion of a body and the population of avatars is representative of a population of individuals [0015]) , and articles data on multiple articles (Pieper: The method also includes obtaining a set of data describing a product to be worn by the individuals in the population of individuals. [0014]) , wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space (Pieper: if the simulation generated at step 420 and analyzed at step 425 determines that the consumer has selected a personal care product that is too small (which would be unconformable) or too large (which may be prone to leak) based on the avatar representation of the individual, then alternatives sizes, products, or product configurations may be recommended, [0059]) , wherein the presenting prompting data includes presenting prompting data that specifies the one or more alternate article (Pieper: In such a case, at step 440, the consumer may select to review a simulation of the recommended changes, and the method 400 may return to step 410 and generate a simulation of the modified product interacting with the avatar representation of the individual [0059]; see Note 5A ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by enabling a user to ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Pieper still fails to teach: wherein the method includes iteratively updating the user data, spaces data, and the articles data of the data repository, wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository, wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open area of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article, wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition. Farshori teaches: wherein the method includes iteratively updating (Farshori: The server computer 112 can repeat one or more of the operations 202-208 repeatedly, if desired. [0073]) the user data, spaces data, and the articles data (see Note 9A ) of the data repository (Farshori: the shopping service 110 can be configured to store the shopping data 116 at a data store 118. [0050]; see Note 9A ) , wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository (Farshori: The use of this [compatibility] data is illustrated and described in more detail below, but briefly this information can be used, for example, to determine if a product being considered for purchase or being purchased can be transported home in a user's vehicle; if the product will fit in a particular home, office, or other location; or the like [0046]; see Note 9B ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Pieper and Farshori still fails to teach: wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open area of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article. wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition. Delgado teaches: wherein the method includes determining that the user has selected a subsequent article for acquisition (Delgado: the user 1250 of FIG. 11A is interested in determining whether a second box can fit within the trunk 1204 of the car 1202 together with the first box [0239]) , in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article (Delgado: the user device 1100 may detect surfaces 1212, 1214, 1216, 1218, 1220 and 1222 of the interior of the trunk 1204, surfaces 1226, 1228, 1230 and 1232 around the edge 1224 of the trunk 1204, [0229]; see Note 10A ) , wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user (Delgado: in order to facilitate an assessment of the fit of one or more virtual objects within an AR space a user may change the display settings (e.g. activate/deactivate the blend feature and/or change the opacity with which 3D representations of virtual objects are displayed) and/or navigate within the AR space in order to view the virtual object(s) from multiple perspectives [0248]; see Note 10B ) ; and wherein the method includes in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user (see Note 10B ), wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article (Delgado: the AR engine 322a may determine a 3D fit of the virtual object within the 3D bounded space by performing collision detection, which detects whether the virtual object fits within the 3D bounded space without colliding with any boundary of the 3D bounded space [0179]) , and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article (Delgado: each of the virtual objects is repositionable in the AR space 1210 independent of the other to allow assessment of the 3D fit of the virtual objects 1260 and 1290 together within the trunk 1204 in different positions [0242]; see Note 10C ) . wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition (see Note 10C ), subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data (see Note 11A ), and prior to the determining that the user has selected the subsequent article for acquisition (see Note 11B ). Note 11A : In Note 10C it was shown that Delgado teaches that the user may add a second item to the trunk and perform assessment of the fit for each item in the trunk. Additionally, in Note 10C, it was shown that the second aggregate space is defined by the presence of the first selected article, requiring transformation of the functional space by inclusion therein of the article as prompted for by the prompting data. Furthermore, as Pieper has taught “wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space”, when the teachings of Pieper are combined with Delgado, it would be obvious to transform the functional space based on the alternate article (which may fit) rather than with the article (already determined to not be able to fit). Note 11B : Delgado teaches: “… the disclosed method may be particularly useful for e-commerce transactions, because it can potentially be used at several points in the ordering, order fulfillment and delivery processes. For example, a merchant operating an online store may include a user-selectable link on their website for a particular household product that, when selected by a consumer that is browsing the website on an AR-capable device, provides the AR-capable device with 3D dimensions of the product that allow the AR-capable device to render a 3D representation or model of the product in an AR space.” [0187], and that “It is noted that an AR system that allows assessment of 3D fit of a virtual object in different positions within a physical environment has applications beyond assisting a customer in assessing fit of a product within the location in which the customer is interested in ultimately using the product. For example, such an AR experience may be advantageous at multiple stages of a supply chain,” and that “For example, the shipping container to be used for transport of an object may vary based on shipping routes, carrier, and composition of a total shipment. This often means that some shipments cannot be delivered as intended as the delivery location is too small for the content being delivered.” [0109]. In other words, Delgado teaches that an issue may arise where an article may not be able to even be shipped because of the dimensions of the article (in [0109]). Delgado also teaches that both the user and the merchant may use a website separate from the store website to assess the fit of the product within a functional space (in [0187]). The present specification teaches that: “In one example, manager system 110 can flag the placement of an online article for acquisition onto an online shopping cart as the selection of such article for acquisition,” [0038]. One of ordinary skill in the art would reasonably conclude, based on the teachings of Delgado, that assessment fitting of a subsequent article could occur before that article is added to the user’s cart on the store website, and furthermore, that it would be obvious to implement a system that enables such functionality, in order to avoid the issue described by Delgado in [0109] cited above. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Delgado with Hazlewood in view of Pieper and Farshori. Assessing fitting the article in the functional space by evaluating a first and second aggregate open space of the functional space relative to article, as in Delgado, would benefit the Hazlewood in view of Pieper and Farshori teachings by enabling a user to add multiple items to a scene and assess the fitting for each item individually . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Assouline (US 20220327608 A1) and Tran (US 9836883 B2; see attached document for paragraph numbers) . Regarding claim 12: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, wherein the machine learning predictive model has been trained with iterations of training data, wherein the iterations of training data include data of prior article acquisitions of the user and additional users. Assouline teaches: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article (Assouline: the expected object module 516 can determine that the sofa and coffee table furniture items have higher scores than the rug furniture item and can in response select only the sofa and coffee table as furniture items to recommend to a user to purchase for inclusion in the room [0109]; see Note 4A ) , and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space (Assouline: The expected object module 516 can also process the 3D mesh representation of the room to compute an amount of available physical space remaining in the room depicted in the image. In response, the expected object module 516 can determine that only the coffee table furniture item can physically fit within the dimensions of the room and that the room cannot physically fit all three missing furniture items. [0111]; see Note 4B ) , wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model (Assouline: In some cases, the disclosed techniques train a neural network classifier to determine the room classification [0016]) , wherein the machine learning predictive model has been trained with iterations of training data (Assouline: multiple classifiers are trained in parallel or sequentially on different sets of training data corresponding to different room classifications. For example, a first classifier can be trained to classify a living room based on a first set of training images that depict different living room features. [0103]) , wherein the iterations of training data include data of article acquisitions of additional users (Assouline: To train the neural network classifier, the disclosed techniques receive training data comprising a plurality of training images and ground truth room classifications for each of the plurality of training images, each of the plurality of training monocular images depicting a different room in a home, [0016]) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Assouline with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Assouline, would benefit the Hazlewood teachings by helping the user select items that will fit in a space more efficiently: “Typically, virtual reality (VR) and augmented reality (AR) systems allow users to add augmented reality elements to their environment […] the user of these systems has to spend a great deal of effort searching through and navigating multiple user interfaces and pages of information to identify an item of interest. Then the user has to manually position the selected item within view. These tasks can be daunting and time consuming, which detracts from the overall interest of using these systems and results in wasted resources” (Assouline, [0014]). Hazlewood in view of Assouline still fails to explicitly teach: wherein the iterations of training data include data of prior article acquisitions of the user and additional users. Tran teaches: wherein the iterations of training data include data of prior article acquisitions of the user (Tran: In some embodiments, the user's purchase history, retained as part of their account information, may be used as training data for a Bayesian network component associated with the outfit suggestion component (331)). Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tran with Hazlewood in view of Assouline. Including data of prior article acquisitions of the user in the training data, as in Tran, would benefit the Hazlewood in view of Assouline teachings by personalizing the recommendations to the user’s specific interests . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Assouline (US 20220327608 A1) , Tran (US 9836883 B2; see attached document for paragraph numbers) and Kruck (US 20230111745 A1) . The Examiner notes that claim 13 contains limitations also seen in claim 12 (see text comparison below). For the content specific to claim 13, see the teachings of Kruck in this section. PNG media_image4.png 468 412 media_image4.png Greyscale Claim 13 compared to claim 12. Regarding claim 13: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), wherein the prompting data prompts for the acquisition of the article (Hazlewood: In some examples, the notification 1130 may also or alternatively provide an option to purchase the item (e.g., when the item fits in the space associated with the projection surface) from the electronic marketplace. (107)) , Hazlewood alone fails to teach the remainder of the limitations of claim 13. Assouline teaches: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article (Assouline: the expected object module 516 can determine that the sofa and coffee table furniture items have higher scores than the rug furniture item and can in response select only the sofa and coffee table as furniture items to recommend to a user to purchase for inclusion in the room [0109]; see Note 4A ) , and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space (Assouline: The expected object module 516 can also process the 3D mesh representation of the room to compute an amount of available physical space remaining in the room depicted in the image. In response, the expected object module 516 can determine that only the coffee table furniture item can physically fit within the dimensions of the room and that the room cannot physically fit all three missing furniture items. [0111]; see Note 4B ) , wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model (Assouline: In some cases, the disclosed techniques train a neural network classifier to determine the room classification [0016]) , wherein the machine learning predictive model has been trained with iterations of training data (Assouline: multiple classifiers are trained in parallel or sequentially on different sets of training data corresponding to different room classifications. For example, a first classifier can be trained to classify a living room based on a first set of training images that depict different living room features. [0103]) , wherein the iterations of training data include data of article acquisitions of additional users (Assouline: To train the neural network classifier, the disclosed techniques receive training data comprising a plurality of training images and ground truth room classifications for each of the plurality of training images, each of the plurality of training monocular images depicting a different room in a home, [0016]) , Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Assouline with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Assouline, would benefit the Hazlewood teachings by helping the user select items that will fit in a space more efficiently: “Typically, virtual reality (VR) and augmented reality (AR) systems allow users to add augmented reality elements to their environment […] the user of these systems has to spend a great deal of effort searching through and navigating multiple user interfaces and pages of information to identify an item of interest. Then the user has to manually position the selected item within view. These tasks can be daunting and time consuming, which detracts from the overall interest of using these systems and results in wasted resources” (Assouline, [0014]). Hazlewood in view of Assouline still fails to explicitly teach: wherein the iterations of training data include data of prior article acquisitions of the user and additional users. Tran teaches: wherein the iterations of training data include data of prior article acquisitions of the user (Tran: In some embodiments, the user's purchase history, retained as part of their account information, may be used as training data for a Bayesian network component associated with the outfit suggestion component (331)), Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tran with Hazlewood in view of Assouline. Including data of prior article acquisitions of the user in the training data, as in Tran, would benefit the Hazlewood in view of Assouline teachings by personalizing the recommendations to the user’s specific interests. Kruck teaches: wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data (Kruck: the recommended replacement item output to the user is the candidate recommendation selected by one of the sub-models shown in FIG. 2, and the recommendation engine 115 uses any feedback related to the candidate recommendation to retrain the corresponding model that produced the candidate [0054]) , wherein the method includes determining that the user has selected a subsequent article for acquisition, (Kruck: For example, the user feedback may be that the user accepts the recommended replacement item as a substitute for the first item, the user selects a different item to replace the first item, or the user cancels the order for the item. [0012]) wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model (Kruck: In some implementations, the model used by the recommendation engine 115 is a neural network [0023]) as trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data (Kruck: A machine learning model can be trained with supervised learning, where the training data includes inputs and desired outputs. The inputs can include, for example, features of products or previously-placed BOPUS orders. [0024]) , and presenting second prompting data to the user in dependence of a result of the performing the subsequent querying of the machine learning predictive model (Kruck: if a user rejects the recommended replacement items, the application can display other candidate replacement items that were ranked lower by the recommendation model [0051]; Kruck: Recommended replacement items can be presented to a user when a user adds a target item to an order cart [0021]) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Kruck with Hazlewood in view of Assouline and Tran. Applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, and performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, as in Kruck, would benefit the Hazlewood in view of Assouline and Tran teachings by enabling the system to learn what replacement items best match a user’s preferences and requirements . 07-21-aia AIA Claim s 14 and 19 are rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Wiesel (US 20220138250 A1; from applicant’s IDS) . Regarding claim 14: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), Hazlewood fails to teach: wherein the method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, wherein the discovering includes looking up dimensional data of multiple candidate articles having a common article classification from a data repository, assessing candidate article fitting of the multiple candidate articles within the functional space, and filtering out a subset of the multiple candidate articles based on finding from the assessing candidate article fitting that the subset of the multiple candidate articles are not qualified for fitting within the functional space. Pieper teaches: wherein method includes finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space (Pieper: if the simulation generated at step 420 and analyzed at step 425 determines that the consumer has selected a personal care product that is too small (which would be unconformable) or too large (which may be prone to leak) based on the avatar representation of the individual, then alternatives sizes, products, or product configurations may be recommended, [0059]; see Note 2A ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Pieper, would benefit the Hazlewood teachings by enabling the user to more easily locate an item that does fit after picking an item that did not fit. Hazlewood in view of Pieper still fails to teach: wherein the discovering includes looking up dimensional data of multiple candidate articles having a common article classification from a data repository, assessing candidate article fitting of the multiple candidate articles within the functional space, and filtering out a subset of the multiple candidate articles based on finding from the assessing candidate article fitting that the subset of the multiple candidate articles are not qualified for fitting within the functional space. Weisel teaches: wherein the discovering includes looking up dimensional data of multiple candidate articles having a common article classification from a data repository (Wiesel: search a digital catalog of clothes for clothing articles that are provided in said particular size [0535]) , assessing candidate article fitting of the multiple candidate articles within the functional space (Wiesel: determine from said dimensions a size of a clothing-article that would match said user (e.g., as mentioned above; for example, by utilizing a pre-defined lookup table that matches a particular clothing size, such as “XL”, with a particular set of dimensions of chest, waist, and/or other body parts) [0535]) , and filtering out a subset of the multiple candidate articles based on finding from the assessing candidate article fitting that the subset of the multiple candidate articles are not qualified for fitting within the functional space (Wiesel: if the system estimates that the matching size should be “XL”, then, excluding from the search results any results of products that are not offered at that size of “XL”, or, by performing a search only for products that are offered at that particular size [0535]) . Regarding claim 19: Claim 19 is substantially similar to claim 14, and is therefore rejected for similar reasons. Claim 19 contains the following notable differences: Claim 19 claims a system instead of a computer implemented method. In the rejection of claim 18, it was shown that Hazlewood teaches a system . 07-21-aia AIA Claim 15 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1) in view of Pieper (US 20080163054 A1), Farshori (US 20190362381 A1; from applicant’s IDS), Delgado (US 20220130126 A1), Assouline (US 20220327608 A1), Tran (US 9836883 B2; see attached document for paragraph numbers) and Kruck (US 20230111745 A1) . The Examiner notes that claim 15 contains limitations also seen in claims 11 and 13 (see text comparison below). PNG media_image5.png 775 415 media_image5.png Greyscale PNG media_image6.png 709 417 media_image6.png Greyscale Claim 15 versus claim 11 on the left, and versus claim 13 on the right. Regarding claim 15: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), wherein the prompting data prompts for the acquisition of the article (Hazlewood: In some examples, the notification 1130 may also or alternatively provide an option to purchase the item (e.g., when the item fits in the space associated with the projection surface) from the electronic marketplace. (107)) , Hazlewood fails to teach the remainder of limitations of claim 15. Pieper teaches: wherein the method includes prior to the determining that the user has selected an article for acquisition (see Note 15A ), establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users (Pieper: receiving a selection of a population of avatars. Each avatar provides a representation of at least a portion of a body and the population of avatars is representative of a population of individuals [0015]) , and articles data on multiple articles (Pieper: The method also includes obtaining a set of data describing a product to be worn by the individuals in the population of individuals. [0014]) , Note 15A : It would be obvious to one of ordinary skill in the art to establish and have ready a database containing available articles prior to allowing a user to select an article for acquisition, because databases are typically used in the art to store large amounts data for systems that interact with many users. Pieper teaches such a setup in [0040]: “Database system 111 may be used to store a collection of information used by virtual reality tool 127 to generate a virtual realty simulation of a product being worn by an avatar 115, or a population of avatars 115. For example, database system 111 may store avatars 115 generated from a group of actual individuals selected to be representative of a population.” To look at it another way, it makes little sense for one of ordinary skill in the art to only establish the database after the user selects an article. Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by enabling a user to ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Pieper still fails to teach: wherein the method includes iteratively updating the user data, spaces data, and the articles data of the data repository, wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and wherein the method includes in dependence on the performing assessing of fitting of the subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article, wherein the second aggregate open space is different from the first aggregate open space as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition, wherein the method includes responsively to the determining that the user has selected the article for acquisition ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, wherein the machine learning predictive model has been trained with iterations of training data, wherein the iterations of training data include data of prior article acquisitions of the user and additional users, wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying. Farshori teaches: wherein the method includes iteratively updating (Farshori: The server computer 112 can repeat one or more of the operations 202-208 repeatedly, if desired. [0073]) the user data, spaces data, and the articles data (see Note 9A ) of the data repository (Farshori: the shopping service 110 can be configured to store the shopping data 116 at a data store 118. [0050]; see Note 9A ) , wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository (Farshori: The use of this [compatibility] data is illustrated and described in more detail below, but briefly this information can be used, for example, to determine if a product being considered for purchase or being purchased can be transported home in a user's vehicle; if the product will fit in a particular home, office, or other location; or the like [0046]; see Note 9B ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Pieper and Farshori still fails to teach: wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and wherein the method includes in dependence on the performing assessing of fitting of the subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article, wherein the second aggregate open space is different from the first aggregate open space as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition, wherein the method includes responsively to the determining that the user has selected the article for acquisition ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, wherein the machine learning predictive model has been trained with iterations of training data, wherein the iterations of training data include data of prior article acquisitions of the user and additional users, wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying. Delgado teaches: wherein the method includes determining that the user has selected a subsequent article for acquisition (Delgado: the user 1250 of FIG. 11A is interested in determining whether a second box can fit within the trunk 1204 of the car 1202 together with the first box [0239]) , in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article (Delgado: the user device 1100 may detect surfaces 1212, 1214, 1216, 1218, 1220 and 1222 of the interior of the trunk 1204, surfaces 1226, 1228, 1230 and 1232 around the edge 1224 of the trunk 1204, [0229]; see Note 10A ) , wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user (Delgado: in order to facilitate an assessment of the fit of one or more virtual objects within an AR space a user may change the display settings (e.g. activate/deactivate the blend feature and/or change the opacity with which 3D representations of virtual objects are displayed) and/or navigate within the AR space in order to view the virtual object(s) from multiple perspectives [0248]; see Note 10B ) ; and wherein the method includes in dependence on the performing assessing of fitting of the subsequent article in the functional space of the user, presenting second prompting data to the user (see Note 10B ), wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article (Delgado: the AR engine 322a may determine a 3D fit of the virtual object within the 3D bounded space by performing collision detection, which detects whether the virtual object fits within the 3D bounded space without colliding with any boundary of the 3D bounded space [0179]) , and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article (Delgado: each of the virtual objects is repositionable in the AR space 1210 independent of the other to allow assessment of the 3D fit of the virtual objects 1260 and 1290 together within the trunk 1204 in different positions [0242]; see Note 10C ) . wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition (see Note 10C ), subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data (see Note 11A ), and prior to the determining that the user has selected the subsequent article for acquisition (see Note 11B ). Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Delgado with Hazlewood in view of Pieper and Farshori. Assessing fitting the article in the functional space by evaluating a first and second aggregate open space of the functional space relative to article, as in Delgado, would benefit the Hazlewood in view of Pieper and Farshori teachings by enabling a user to add multiple items to a scene and assess the fitting for each item individually. Hazlewood in view of Pieper, Farshori, and Delgado fails to teach: wherein the method includes responsively to the determining that the user has selected the article for acquisition ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, wherein the machine learning predictive model has been trained with iterations of training data, wherein the iterations of training data include data of prior article acquisitions of the user and additional users, wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying. Assouline teaches: wherein the method includes ascertaining that the user will acquire a complementary article that is complementary to the article (Assouline: the expected object module 516 can determine that the sofa and coffee table furniture items have higher scores than the rug furniture item and can in response select only the sofa and coffee table as furniture items to recommend to a user to purchase for inclusion in the room [0109]; see Note 4A ) , and wherein the assessing fitting includes performing assessment of fitting of the article with the complementary article in the functional space (Assouline: The expected object module 516 can also process the 3D mesh representation of the room to compute an amount of available physical space remaining in the room depicted in the image. In response, the expected object module 516 can determine that only the coffee table furniture item can physically fit within the dimensions of the room and that the room cannot physically fit all three missing furniture items. [0111]; see Note 4B ) , wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model (Assouline: In some cases, the disclosed techniques train a neural network classifier to determine the room classification [0016]) , wherein the machine learning predictive model has been trained with iterations of training data (Assouline: multiple classifiers are trained in parallel or sequentially on different sets of training data corresponding to different room classifications. For example, a first classifier can be trained to classify a living room based on a first set of training images that depict different living room features. [0103]) , wherein the iterations of training data include data of article acquisitions of additional users (Assouline: To train the neural network classifier, the disclosed techniques receive training data comprising a plurality of training images and ground truth room classifications for each of the plurality of training images, each of the plurality of training monocular images depicting a different room in a home, [0016]) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Assouline with Hazlewood. Finding by the assessing fitting that the article is not qualified for fitting in the functional space, and, based on the finding, discovering one or more alternate article for fitting in the functional space, as in Assouline, would benefit the Hazlewood teachings by helping the user select items that will fit in a space more efficiently: “Typically, virtual reality (VR) and augmented reality (AR) systems allow users to add augmented reality elements to their environment […] the user of these systems has to spend a great deal of effort searching through and navigating multiple user interfaces and pages of information to identify an item of interest. Then the user has to manually position the selected item within view. These tasks can be daunting and time consuming, which detracts from the overall interest of using these systems and results in wasted resources” (Assouline, [0014]). Hazlewood in view of Pieper, Farshori, Delgado, and Assouline still fails to explicitly teach: wherein the iterations of training data include data of prior article acquisitions of the user and additional users, wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying. Tran teaches: wherein the iterations of training data include data of prior article acquisitions of the user (Tran: In some embodiments, the user's purchase history, retained as part of their account information, may be used as training data for a Bayesian network component associated with the outfit suggestion component (331)). Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Tran with Hazlewood in view of Pieper, Farshori, Delgado, and Assouline. Including data of prior article acquisitions of the user in the training data, as in Tran, would benefit the Hazlewood in view of Pieper, Farshori, Delgado, and Assouline teachings by personalizing the recommendations to the user’s specific interests. Hazlewood in view of Pieper, Farshori, Delgado, Assouline, and Tran still fails to teach: wherein the method includes responsively to the determining that the user has selected the article for acquisition ascertaining that the user will acquire a complementary article that is complementary to the article, and wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data, wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying. Kruck teaches: wherein the method includes responsively to the determining that the user has selected the article for acquisition ( Kruck: In some implementations, a recommendation engine identifies a first item, purchased by a user for in-store fulfillment, that is unfulfillable at a time of intended fulfillment [0012] ) ascertaining that the user will acquire a complementary article that is complementary to the article (Kruck: The recommendation engine applies a trained model to select a recommended replacement item for the first item [0012]) , and wherein the method includes subsequent to the presenting the prompting data, applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data (Kruck: the recommended replacement item output to the user is the candidate recommendation selected by one of the sub-models shown in FIG. 2, and the recommendation engine 115 uses any feedback related to the candidate recommendation to retrain the corresponding model that produced the candidate [0054]) , wherein the method includes responsively to the determining that the user has selected the subsequent article for acquisition, performing subsequent querying of the machine learning predictive model (Kruck: In some implementations, the model used by the recommendation engine 115 is a neural network [0023]) which at a time of the subsequent querying has been trained by data for training that includes the training data specifying acquisition of the article by the user as prompted for by the prompting data (Kruck: A machine learning model can be trained with supervised learning, where the training data includes inputs and desired outputs. The inputs can include, for example, features of products or previously-placed BOPUS orders. [0024]) , wherein the presenting the second prompting data to the user is performed in dependence of a result of the subsequent querying (Kruck: if a user rejects the recommended replacement items, the application can display other candidate replacement items that were ranked lower by the recommendation model [0051]; Kruck: Recommended replacement items can be presented to a user when a user adds a target item to an order cart [0021]) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Kruck with Hazlewood in view of Pieper, Farshori, Delgado, Assouline, and Tran. Applying a subsequent iteration of training data to the machine learning predictive model, the subsequent iteration of training data specifying acquisition of the article by the user as prompted for by the prompting data, and performing assessment of fitting of the article with the complementary article in the functional space, wherein the ascertaining that the user will acquire a complementary article that is complementary to the article includes querying a machine learning predictive model, as in Kruck, would benefit the Hazlewood in view of Pieper, Farshori, Delgado, Assouline, and Tran teachings by enabling the system to learn what replacement items best match a user’s preferences and requirements . 07-21-aia AIA Claim 16 is rejected under 35 U.S.C 103 as being unpatentable over Hazlewood (US 9715865 B1; see attached document for paragraph numbers) in view of Farshori (US 20190362381 A1; from applicant’s IDS) and Delgado (US 20220130126 A1) . The Examiner notes that claim 16 contains limitations also seen in claim 11 (see text comparison below). PNG media_image7.png 642 424 media_image7.png Greyscale Comparison of claim 16 versus claim 11. Claim 16 contains similar limitations to claim 11 but omits limitations iteratively updating the user, spaces, and articles data. Regarding claim 16: Hazlewood teaches: The computer implemented method of claim 1 (as shown above), wherein the prompting data prompts for the acquisition of the article (Hazlewood: In some examples, the notification 1130 may also or alternatively provide an option to purchase the item (e.g., when the item fits in the space associated with the projection surface) from the electronic marketplace. (107)) , Hazlewood fails to teach the remainder of the limitations of claim 16. Hazlewood fails to teach: wherein the method includes iteratively updating the user data, spaces data, and the articles data of the data repository, wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository, wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open area of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article, wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition. Farshori teaches: wherein the method includes iteratively updating (Farshori: The server computer 112 can repeat one or more of the operations 202-208 repeatedly, if desired. [0073]) the user data, spaces data, and the articles data (see Note 9A ) of the data repository (Farshori: the shopping service 110 can be configured to store the shopping data 116 at a data store 118. [0050]; see Note 9A ) , wherein the assessing fitting includes calling dimensional data of the functional space and the article from the data repository (Farshori: The use of this [compatibility] data is illustrated and described in more detail below, but briefly this information can be used, for example, to determine if a product being considered for purchase or being purchased can be transported home in a user's vehicle; if the product will fit in a particular home, office, or other location; or the like [0046]; see Note 9B ) . Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Pieper with Hazlewood. Establishing a data repository having user data on multiple users, spaces data on functional spaces of the multiple users and articles data on multiple articles, as in Pieper, would benefit the Hazlewood teachings by ensuring the system has access to a central location where user and article data can be readily accessed. Hazlewood in view of Farshori still fails to teach: wherein the method includes determining that the user has selected a subsequent article for acquisition, wherein the method includes in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article, wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user, and in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user, wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open area of the functional space relative to article, and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article. wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition, subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data, and prior to the determining that the user has selected the subsequent article for acquisition. Delgado teaches: wherein the method includes determining that the user has selected a subsequent article for acquisition (Delgado: the user 1250 of FIG. 11A is interested in determining whether a second box can fit within the trunk 1204 of the car 1202 together with the first box [0239]) , in response to the determining that the user has selected the subsequent article for acquisition, identifying the functional space of the user associated to the subsequent article (Delgado: the user device 1100 may detect surfaces 1212, 1214, 1216, 1218, 1220 and 1222 of the interior of the trunk 1204, surfaces 1226, 1228, 1230 and 1232 around the edge 1224 of the trunk 1204, [0229]; see Note 10A ) , wherein the method includes in response to the identifying the functional space of the user associated to the subsequent article, performing assessment fitting of the subsequent article in the functional space of the user (Delgado: in order to facilitate an assessment of the fit of one or more virtual objects within an AR space a user may change the display settings (e.g. activate/deactivate the blend feature and/or change the opacity with which 3D representations of virtual objects are displayed) and/or navigate within the AR space in order to view the virtual object(s) from multiple perspectives [0248]; see Note 10B ) ; and wherein the method includes in dependence on the performing assessing of fitting of subsequent article in the functional space of the user, presenting second prompting data to the user (see Note 10B ), wherein the assessing fitting the article in the functional space includes evaluating a first aggregate open space of the functional space relative to article (Delgado: the AR engine 322a may determine a 3D fit of the virtual object within the 3D bounded space by performing collision detection, which detects whether the virtual object fits within the 3D bounded space without colliding with any boundary of the 3D bounded space [0179]) , and wherein the performing assessing of fitting of the subsequent article in the functional space includes evaluation a second aggregate open space of the functional space with respect to the subsequent article (Delgado: each of the virtual objects is repositionable in the AR space 1210 independent of the other to allow assessment of the 3D fit of the virtual objects 1260 and 1290 together within the trunk 1204 in different positions [0242]; see Note 10C ) . wherein the second aggregate open area is different from the first aggregate area as a result of dimensional data of the functional space of the user within the spaces data of the data repository having been updated by the iteratively updating subsequent to the determining that the user has selected the article for acquisition (see Note 10C ), subsequent to transformation of the functional space by inclusion therein of the alternate article as prompted for by the prompting data (see Note 11A ), and prior to the determining that the user has selected the subsequent article for acquisition (see Note 11B ). Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to combine the teachings of Delgado with Hazlewood in view of Pieper and Farshori. Assessing fitting the article in the functional space by evaluating a first and second aggregate open space of the functional space relative to article, as in Delgado, would benefit the Hazlewood in view of Pieper and Farshori teachings by enabling a user to add multiple items to a scene and assess the fitting for each item individually. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT ALEXANDER PROVIDENCE whose telephone number is (571)270-5765. The examiner can normally be reached Monday-Thursday 8:30-5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, King Poon can be reached at (571)270-0728. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /VINCENT ALEXANDER PROVIDENCE/Examiner, Art Unit 2617 /KING Y POON/Supervisory Patent Examiner, Art Unit 2617 Application/Control Number: 18/360,155 Page 2 Art Unit: 2617 Application/Control Number: 18/360,155 Page 3 Art Unit: 2617 Application/Control Number: 18/360,155 Page 4 Art Unit: 2617 Application/Control Number: 18/360,155 Page 5 Art Unit: 2617 Application/Control Number: 18/360,155 Page 6 Art Unit: 2617 Application/Control Number: 18/360,155 Page 7 Art Unit: 2617 Application/Control Number: 18/360,155 Page 8 Art Unit: 2617 Application/Control Number: 18/360,155 Page 9 Art Unit: 2617 Application/Control Number: 18/360,155 Page 10 Art Unit: 2617 Application/Control Number: 18/360,155 Page 11 Art Unit: 2617 Application/Control Number: 18/360,155 Page 12 Art Unit: 2617 Application/Control Number: 18/360,155 Page 13 Art Unit: 2617 Application/Control 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