DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination (“RCE”) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 12, 2026 has been entered.
Acknowledgements
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the RCE noted above..
Claims 1-12, 15-26, and 29-34 are pending are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12, 15-26, and 29-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: claims 15-26 are directed to a method and claims 1-12 and 29-34 are directed to a product (device & computer readable medium). Therefore, these claims fall within the four statutory categories of invention.
Step 2A, prong 1: Claim 1, for example, for example, recites the abstract idea of recommending to a user a list of items/products based on a profile. This idea is described by the following claim elements:
receiving intent information associated with an interaction of a user with a retail entity;
obtaining a set of recommendation parameters;
generating recommendation information associated with the user, the recommendation information being generated based at least in part on the intent information and the set of recommendation parameters; and
obtain first location information associated with a first location of the user; obtain second location information associated with one or more recommended items associated with the retail entity;
generate a sorted list of recommended items, associated with the retail entity, based at least in part on the recommendation information, the first location information, and the second location information;
generate a path recommendation, associated with the sorted list of recommended items, based at least in part on the second location information; and
obtain third location information associated with a second location of the user in the retail entity
update the sorted list of recommended items and the path recommendation in real time or near real time based at least in part the third location information.
The noted steps/functions are directed to Certain Methods of Organizing Human Activity grouping of abstract ideas as they involve or managing personal behavior or relationships or interactions between people. These steps involve receiving intent/profile information of a user, obtaining recommendation parameters, generating and providing to the user a list of recommended items based on the intent/profile information and the recommendation parameters and update the list of recommended items based on location information.
Step 2A, prong 2: claims 1, 15, 29 & 30 recite additional elements that fail to integrate the abstract idea into practical application.
Claims 1, 15, 29 & 30 recite the following additional limitations:
one or more memories/ A non-transitory computer-readable medium storing instructions
one or more processors, coupled to the one or more memories,
a device/ apparatus
The device/apparatus comprising one or more processors, memories or computer-readable medium storing instructions, is merely used as a tool to implement the abstract idea. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on the locating system, or merely uses the locating system as a tool to perform an abstract idea, cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements noted above are recited at a high level of generality, and comprises only a microprocessor and memory to simply perform the generic computer functions of receiving, identifying, calculating, determining & comparing assets data and assigning assets to a room.
Additionally, ¶ [0020] of the application as filed indicates that the device 110 for generating recommendation information is general purpose computer(…computing device, such as a UE, a mobile phone (e.g., a smart phone, a radiotelephone, and/or the like), a laptop computer, a tablet computer, a handheld computer, a desktop computer, a gaming device, a wearable communication device (e.g., a smart wristwatch, a pair of smart eyeglasses, and/or the like), or the like.) Generic computers performing generic computer functions, alone, do not integrate the claimed abstract idea into a practical application.
Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination does not integrate the abstract idea into a practical application. The additional elements fail to provide a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, ( 4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04, (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2).
Step 2B: claim fails to recite additional elements that amount to an inventive concept.
For the reasons identified with respect to Step 2A, prong 2, claims 1, 15. 29 & 30 fail to recite additional elements that amount to an inventive concept. For example, use of a device/apparatus or other machinery in its ordinary capacity for economic or other tasks (e.g., recommending to a user a list of items/products based on a profile) or simply adding a general-purpose computer system or computer component after the fact to an abstract idea (e.g., a fundamental economic practice) does not integrate a judicial exception into a practical application or provide significantly more (see MPEP 2106.05(g)).
Furthermore, the additional claimed elements, noted above, when viewed individually and as an ordered combination do not provide significantly more than the abstract idea. The additional elements fail to (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; ( 4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the fie Id; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of the MPEP 2106.0S(a-h).
The dependent claims further describe the abstract idea of recommending to a user a list of items/products based on a profile. These claims do not include new additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, these dependent claims are also not patent eligible.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
means for receiving intent information associated with an interaction of a user with a retail entity;
means for obtaining a set of recommendation parameters; means for generating recommendation information associated with the user, the recommendation information being generated based at least in part on the intent information and the set of recommendation parameters; and
means for providing the recommendation information for display to the user, the recommendation information including a list of recommended items.
The examiner interprets the corresponding structure to be the apparatus/device 200 including software module for implementing the noted above functions (receiving, obtaining and providing).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-12, 15-26, and 29-34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, for example, recites “obtain second location….” And “obtain third location information associated with a second location….”. It would be unclear to a person of ordinary skill in the art to ascertain whether the “second location” recited in both limitations noted above are the same or different. Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-13, 15-27, 29, 30 & 34 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20190163985 Al) (“Wang”) and further in view of Tiku et al (US 20240070729 A1) (“Tiku”) and further in view of Kurani et al (US 20200387948 A1) (“Kurani”).
As per claims 1, 15, 29 & 30, Wang discloses:
one or more memories; and one or more processors, coupled to the one or more memories (¶¶ [0044]- [0046]; Figs. 1A–1C, 4–5) (Wang describes frontend/backend computing devices with processors and memory that store and execute recommendation-system modules).
receive intent information associated with an interaction of a user with a retail entity (¶¶ [0043], [0048], [0054], [0063]–[0066]) (Wang detects in-store inspection events and close-proximity interactions, and ingests session signals capturing user intent).
obtain a set of recommendation parameters (¶¶ [0050], [0055]–[0058], [0070]) (Wang obtains constraint information, user preferences, inventory, pricing, and model selection factors as recommendation parameters).
generate recommendation information, associated with the user, based at least in part on the intent information and the set of recommendation parameters (¶¶ [0055], [0058], [0061], [0070]; Fig. 2) (Wang’s product recommendation module generates personalized item lists/bundles and explanatory reasons based on captured intent and parameters).
obtain first location information associated with a first location of the user (¶¶ [0049], [0053]) (Wang uses store/deployment location and recognizes in-store presence as location context associated with the user).
obtain second location information associated with one or more recommended items associated with the retail entity (¶ [0053]) (Wang provides maps/directions to recommended products, implying use of product display/location information within the retail environment).
obtain third location information associated with a second location ofthe user in the retail entity (¶¶ [0100])
update the sorted list of recommended items and the path recommendation […] based at least in part the third location information (Fig. 2; ¶ [0053]) (Wang presents recommendation lists and reasons via UI and can display/print directions or maps to product locations).
Wang does not teach:
generate a sorted list of recommended items, associated with the retail entity, based at least in part on the recommendation information, the first location information, and the second location information.
generate a path recommendation, associated with the sorted list of recommended items, based at least in part on the second location information.
Tiku, however, teaches:
obtain first location information associated with the user (¶¶ [0036]–[0037], [0068]–[0072]) (Tiku detects that the XR device is within a physical retail store based on a geographic location associated with the device, thereby obtaining first location information associated with the user).
obtain second location information associated with one or more recommended items associated with the retail entity (¶¶ [0035]–[0036], [0040]) (Tiku retrieves store mapping information indicating aisles/shelves and corresponding items, i.e., second location information associated with recommended items).
generate a sorted list of recommended items, associated with the retail entity, based at least in part on the recommendation information, the first location information, and the second location information (¶¶ [0010], [0037]–[0041]) (Tiku orders/optimizes the recommended items by proximity using the user/device location and item locations to minimize walking time and improve traversal efficiency).
generate a path recommendation, associated with the sorted list of recommended items, based at least in part on the second location information (¶¶ [0040]–[0041], [0058]–[0060]) (Tiku generates an in-store navigation path that directs the user via overlayed audio-visual cues to locations at which the items are held, producing a multi-stop route based on item location information).
update the sorted list of recommended items and the path recommendation for display to the user (¶¶ [0040]–[0041], [0068]–[0072]) (Tiku presents the ordered items and the navigation overlays via the XR device interface, displaying both the sorted list and the path recommendation to the user).
It would have been obvious to one of ordinary skill in the art to incorporate Tiku’s device geolocation, item-level store mapping, proximity-based ordering, and in-store path navigation into Wang’s personalized recommendation system to reduce shopping time and enhance in-store user experience, as expressly taught by Tiku’s objective to optimize/shorten user routes (Tiku ¶ [0010]). Under KSR, combining known location-aware sorting and path-planning techniques with an existing recommendation engine is a predictable design choice yielding expected benefits.
Wang/Tiku may not expressly disclose update the sorted list of recommended items and the path recommendation in real time or near real time based at least in part the third location information.
Kurani, however, clearly disclose update the sorted list of recommended items and the path recommendation in real time or near real time based at least in part the third location information ((¶¶ [0015], []; The user is provided with customized in-store navigation at a mobile device of the user based on the shopping list and a current layout of the merchant location. The user is also provided with real-time shopping route updates based on the items remaining on the shopping list and a current location of the user mobile device.)
It would have been obvious to one of ordinary skill in the art to incorporate Kurani real-time shopping route updates into Wang’s personalized recommendation system to reduce shopping time and enhance in-store user experience, as expressly taught by Kurani’s system to optimize/shorten user routes (Kurani ¶ [0037]).
As per claims 2 & 16, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the set of recommendation parameters includes constraint information (¶¶ [0050], [0055]- [0058], [0070]);
As per claims 3 & 17, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the set of recommendation parameters includes positive bias information associated with the retail entity (¶¶ [0052], [0064], [0082]- [0084]).
As per claims 4 & 18, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the positive bias information indicates a prioritization that is based at least in part on at least one of revenue information associated with a group of items, margin information associated with the group of items, or inventory information associated with the group of items (¶¶ [0040], [0049], [0050]).
As per claims 5 & 19, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the set of recommendation parameters includes negative bias information associated with the retail entity (¶¶ [0052], [0064], [0082]- [0084]).
As per claims 6 & 20, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the negative bias information indicates a prioritization that is based at least in part on at least one of revenue information associated with a group of items, margin information associated with the group of items, or inventory information associated with the group of items (¶¶ [0040], [0049], [0050]).
As per claims 7 & 21, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the set of recommendation parameters includes inventory information associated with the retail entity (¶¶ [0040], [0049], [0050]).
As per claims 8 & 22, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the set of recommendation parameters includes preference information associated with the user (¶¶ [0042], [0045], [0048]).
As per claims 9 & 23, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the recommendation information includes cost information (e.g. price/discount information) associated with the list of recommended items (¶¶ [0055], [0082]).
As per claims 10 & 24, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the recommendation information includes reasoning information associated with an item included in the list of recommended items (fig. 2 & related text; Please take a look at the following models. They have I been very popular among young professionals with families. I matched I styles and colors of these appliances to the kitchen appliances you I purchase previously. But other color and style choices are also I available.).
As per claims 11 & 25, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the one or more processors are further configured to receive target information associated with the interaction, the target information including a list of target items, wherein the recommendation information associated with the user is generated further based at least in part on the target information (¶ [0063], fig. 3 & related text).
As per claims 12 & 26, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses determine a list of item types associated with an item, provide, for display to the user, the list of item types associated with the item, and identify, based at least in part on user input, a recommended item type from the list of item types, wherein the list of recommended items includes the item and an indication of the recommended item type associated with the item (¶¶ [0061]- The frontend information exchange system proceeds to generated recommendations based on the above processing models and logic, and outputs the following cross-sell and upsell recommendations "Ok. Would you like to also look at some air conditioners? We currently have some great deals for air conditioner and water heater bundles." When Mr. Ma consents to seeing some recommendations, the frontend information exchange system provides the recommended bundle deals to Mr. Ma, and also informs Mr. Ma that the features of the recommended models are suitable for young professionals with families).
As per claim 34, Wang/Tiku/Kurani discloses as shown above.
Wang further discloses wherein the path recommendation includes information that indicates a physical pathway through the retail entity (Wang expressly teaches navigation/path guidance and explicitly states printing or providing “a map or directions to the recommended product” and that the frontend can “move and guide the customer to a recommended product”, ¶0053).
Alternatively, Pedersen below teaches presenting a store map showing item locations when the device is within a defined proximity (Pedersen ¶0030, claims 6/13/19).
Claims 31 & 32 are rejected under 35 U.S.C. § 103 as being unpatentable over Wang (US 2019/0163985 A1) in view of Tiku (US 2018/0356012 A1) in view of Kurani and further in view of Murakhovs'ka et al (US 20240428068 A1) (“Murakhovs'ka”).
As per Claim 31, Wang/Tiku/Kurani discloses as shown above.
Wang further teaches generating recommendation information for a user in a retail context based on in-person close-proximity interactions, inspection events, user profiles, and location data, and outputting product recommendations via a frontend information exchange system (e.g., ¶¶ [0009]–[0011], [0063]–[0068]).
Tiku further teaches use of deep learning accelerators and neural network model families (e.g., CNNs, RNNs, GANs) to perform recommendation-related tasks such as identifying stores/items and generating recommendations (¶¶ [0017]–[0018]).
Wang/Tiku/Kurani does not teach wherein the recommendation information is generated using a large language model (LLM))
Murakhovs'ka, however, teaches use of large language models (LLMs), including transformer-based architectures (e.g., GPT-3, T5), for conversational recommendation agents, Seller/Shopper bot simulations, product catalog/metadata generation, and recommendation output generation (¶¶ [0018], [0023], [0025]–[0027], [0029], [0031]–[0033], [0042], [0045]–[0046], [0065], [0080], [0090]–[0092]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to implement Wang’s recommendation generation component using an LLM as taught by Murakhovs'ka to improve natural language understanding, conversational recommendation quality, and human-readable recommendation outputs. Tiku provides the general ML infrastructure and hardware acceleration context; Murakhovs'ka supplies the specific model class (LLM) and its retail recommendation application. Substituting an LLM for Wang’s existing recommendation engine is a known and predictable design choice yielding improved performance.
As per Claim 32, Wang/Tiku/Kurani discloses as shown above.
Wang further teaches generating sorted recommendation lists that include related or bundled products based on user intent, profiles, and prior purchases (e.g., cross-sell/upsell strategies) (¶¶ [0050], [0058] [0068]–[0070]).
Tiku teaches ML-based recommendation tasks and producing organized recommendation outputs, including determining recommended items associated with categories (¶¶ [0017]– [0018]).
Wang/Tiku/Kurani does not expressly teach wherein the sorted list of recommended goods includes one or more supplemental items to an item indicated by the user.
Murakhovs’ka teaches LLM-based conversational agents recommending complementary or alternative products during seller-shopper dialogues, generating product lists and metadata, and making bundle/cross-sell suggestions (¶¶ [0023], [0025]- [0027], [0029], [0031]– [0033], [0045], [0058], [0091]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combining Wang’s list generation with Tiku’s ML tasks and Murakhovs’ka’s LLM-driven conversational recommendation techniques to include supplemental items in a sorted recommendation list to improve relevance and commercial effectiveness. This is a routine and predictable enhancement in recommendation systems.
Claim 33 is rejected under 35 U.S.C. § 103 as being unpatentable over Wang (US 20190163985 A1) in view of Tiku et al (US 20240070729 A1) in view of Kurani et al (US 20200387948 A1) (“Kurani”). and further in view of Pederson et al (US 20190057433 A1) (“Pederson”).
As per claim 33, Wang/Tiku/Kurani discloses as shown above.
Wang further teaches wherein the sorted list of recommended items (listing of multiple products) and the path recommendation are provided when a location of the user is within a threshold […] (proximity threshold) (¶¶ [0053], [0063], [0064])
Wang further teaches location-aware recommendations and navigation/path guidance in a retail environment tied to store context and item locations (¶¶ [0053], [0068]– [0069]).
Tiku teaches ML/location-aware systems for store/item identification and related proximity-triggered behaviors (¶¶ [0017]– [0018]).
Wang/Tiku/Kurani does not teach: list of items and path recommendation are provided when a location of the user is within a threshold distance of a location of the retail entity.
Pederson, however, clearly discloses list of items and path recommendation are provided when a location of the user is within a threshold distance of a location of the retail entity (¶ [0030], claims 6, 13, 19).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Wang’s in-store recommendation and navigation teachings with Pedersen’s explicit store-level proximity trigger (geo-fencing/beacons) to present recommendations only when the user is near the retail location because doing so is a routine and predictable way to improve contextual relevance and reduce irrelevant notifications. Tiku supplies conventional ML/infrastructure support for implementing the recommendation and store/item identification functions, and thus one of ordinary skill would have expected the combined system to operate predictably when conditioned on a proximity/geofence trigger.
Response to Arguments
Applicant's arguments filed September 29, 2025 have been fully considered but they are not persuasive.
101 Rejection
Applicant argues (page 14):
The Office Action asserts that the steps could be performed mentally or manually using pen and paper. See Office Action, page 4. However, amended claim 1 requires obtaining first, second, and third location information and updating both a sorted list of recommended items and a path recommendation in real time or near real time based at least in part on the third location information. Performing such real-time or near real-time updates based on changing location information is not a mental process and cannot practically be carried out in the human mind. The amended claim thus recites machine-implemented processing tied to real-time location information…..
The examiner, however, respectfully disagrees. Under the broadest reasonable interpretation, obtaining location information and updating an item list based on the obtained location does not require a machine. But even if a machine is required, the functions of obtaining location information and updating/soring a list of items based on the location information in real time are generic computer functions that do not require more than a generic computer for implementation. This is consistent with ¶ [0020] of the specification which states that a general-purpose computer is used for implementing the invention. Merely using generic processors and location data to update lists and paths in real time describes a conventional application of an abstract idea, not a specific technological improvement. The claim lacks disclosure of concrete algorithms, sensor-fusion methods, or novel routing/map-representation techniques that would meaningfully integrate the abstract idea into a patent-eligible practical application.
The asserted combination is described at a high level in the specification and amounts to applying known recommendation, updating/sorting in real time and location techniques on generic hardware. The claim does not recite specific, unconventional technical steps (e.g., novel algorithms, sensor-fusion, or data structures) that would render the combination “significantly more” than the abstract idea.
112(f) Rejection
The examiner interprets the corresponding structure to be the apparatus/device 200 including software module for implementing the noted above functions (receiving, obtaining and updating…etc).
103 Rejection
Applicant’s arguments with respect to the claims have been considered but are moot in view of the new ground of rejection shown above.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Perks et al. (US 20240070729 Al):
In some implementations, an extended reality (XR) device may receive, via an interface of the XR device, an indication of items to be purchased. The XR device may identify one or more physical retail stores that carry the items. The XR device may identify one or more recommended items based on the theme. The XR device may provide, via the interface, a list of the one or more physical retail stores. The XR device may detect that the XR device is within a physical retail store, of the one or more physical retail stores, based on a geographic location associated with the XR device. The XR device may provide, via the interface, an in-store navigation path to direct a user of the XR device via overlayed audio-visual cues to locations within the physical retail store at which the items are held.
US 20230118119 A1 discloses:
System, device, and method of Augmented Reality based mapping of a venue and navigation within a venue. A method includes: performing a crowd-sourced mapping process, that maps a retail store and maps particular products sold within that retail store, based on computer-vision analysis of a plurality of images captured by a plurality of end-user devices of customers within that retail store; and generating a representation of a store map reflecting actual real-time location of particular products within that retail store. Turn-by-turn walking directions are provided, to guide the user from his current in-store location towards a destination product within that retail store. Augmented Reality promotions, advertisements and marketing content elements, route guidance, and other content are generated and displayed on the end-user device.
US 20070112729 A1
[0042] Search queries can be saved and the corresponding results can be recalled and displayed in text or map view according to the current availability of the item. In some instances, a seller may move his item to a different location from the last time the user viewed the item. If the seller changes or updates the location of the item while the user is viewing the item, then the map display can be automatically updated or refreshed in near real-time so that the user is provided with the most up-to-date information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAMON OBEID whose telephone number is (571)270-1813. The examiner can normally be reached 8 AM- 5 PM.
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, Namrata Boveja can be reached at (571) 272-8105. 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.
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
Prosecution Insights