DETAILED ACTION
This rejection is in response to Request for Continued Examination filed 11/13/2025.
Claims 1-20 are currently pending and have been rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/13/2025 has been entered.
Response to Arguments
Applicant's arguments filed 11/13/2025 have been fully considered but they are not persuasive.
With respect to applicant’s arguments on pages 12-15 of remarks filed 11/13/2025 that the claims are directed to a practical application because the claims are directed to improvements to internet searching because determining an item from unstructured text prior to identifying identifiers provides a faster and computationally efficient method to select items for recommendations that are relevant to the user and the technology eliminates or reduces repetitive user queries, filters, processing power, network bandwidth, and memory consumption, Examiner respectfully disagrees.
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. See MPEP § 2106.05(a).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) and 2106.05(a)(II).
Applicant states that the following paragraphs of the specification recites an improvement to technology:
[0019]:… By determining that an item listing comprises compatible items and subsequently identifying the corresponding identifiers within a plurality of item listings identified as having compatible items (rather than identifying identifiers in a population of item listings with item listings that do not include compatible items), the technology disclosed herein provides a faster and computationally efficient method for selecting compatible items for bundle recommendations. Additionally, by identifying identifiers of the compatible products, the technology disclosed herein provides a faster and computationally efficient method compared to methods that identify related products based on terms in the item description (e.g., colors of the item in the item description and length measurements of the item in the item description) rather than identifying the identifiers of the compatible items in the unstructured text of item descriptions.
[0023] Aspects of the technology described herein provide a number of improvements over existing search technologies. For instance, computing resource consumption is improved relative to existing technologies. In particular, determining that unstructured text of an item description for the item listing includes compatible items prior to identifying identifiers for the compatible items provides a faster and computationally efficient method for selecting compatible items for bundle recommendations, since the system is identifying identifiers based on determining the item listing comprises compatible items, rather than processing a total population of item listings that includes item listings that do not have compatible items listed within the item description. As such, aspects of the present technology eliminate or at least reduce excessive processing power, network bandwidth, throughput, memory consumption, and so forth. Furthermore, aspects of the present technology eliminate or at least reduce the repetitive user queries and filter selections of existing search technologies because the bundle recommendations of the present technology comprise results that more closely correspond to a particular intent of the user.
Paragraphs [0019] and [0023] of Applicant’s specification and the claims appear to provide an improvement to solving a commercial problem of selecting compatible items for bundle recommendations by determining that unstructured text of an item description for the item listing includes compatible items prior to identifying identifiers for the compatible items. However, the specification does not provide a technical explanation of the asserted improvement or detail on how to eliminate or at least reduce excessive processing power, network bandwidth, and memory consumption. The claim merely uses the computer as a tool to determine compatible items and provide the compatible items as a recommendation to a user device. The claims do not improve technology because the specification explicitly sets forth an improvement but in a conclusory manner without the detail necessary to be apparent to a person of ordinary skill in the art on how processing, network bandwidth, and memory consumption are improved. Therefore, after evaluating the specification and the claims, the claims do not appear to improve technology but rather use the computer as a tool to select compatible items for bundle recommendations.
With respect to applicant’s arguments on pages 16-18 of remarks filed 11/13/2025 that Wu and Shukla do not teach the classification model executed prior to the natural language processing model, Examiner respectfully disagrees.
Applicant’s arguments with respect to claim amendments 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.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 8, and 15 recite: “wherein the classification model is executed prior to the natural language processing model to reduce a search space for identifier extraction (emphasis added),” which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that Applicant had possession of at the time the application was filed. A review of the specification does not disclose the term “search space for identifier extraction” or any link between the timing of execution of the classification model being executed prior to the natural language processing model reducing a search space for identifier extraction.
Applicant’s specification in paragraph [0042] states that: “the classification model can extract…identifiers.” Applicant’s specification in paragraph [0060]: states that “machine learning natural language processing is used for identifying compatible item listings based on the identifiers extracted.” However, applicant’s specification is silent regarding the sequence of the execution of the classification model prior to the natural language processing model reducing a search space for identifier extraction. Therefore, the claims fail to comply with the written description requirement because the claims contain subject matter which was not described in the specification. Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112(b)
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 1-20 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.
Independent claim 1 recites: extracting from the unstructured text, a brand identifier and a model identifier for the compatible item… utilizing the extracted model identifier and brand identifier, rendering said claims indefinite because it is unclear whether a brand identifier is the same or different from the subsequent recitation of brand identifier. Appropriate correction or clarification is required.
Claims 9, 13, and 19 recite: a model identifier, rendering said claims indefinite because it is unclear whether the model identifier recited in independent claims 8 and 15 is the same or different from a model identifier recited in claims 9, 13, and 19. Appropriate correction or clarification is required.
There is insufficient antecedent basis for the following limitations in:
Claims 1, 8, and 15 recite:
the extracted model identifier;
the executed search query;
Claim 2 and 7 recite:
the identifiers
Claims 2, 4, 7, 10, 11, 14, 16, and 19 recite
the first compatible item;
Claims 10, 13, 14, and 16 recite:
the first identifier;
Claims 2 and 4 recite:
the inventory;
Claims 4 and 7 recite:
the compatible items.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-7 are directed to a method, claims 8-14 are directed to a non-transitory computer storage media, and claims 15-20 are directed to a system each of which falls within one of the four statutory categories of inventions (process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, the abstract idea includes:
accessing,…,an item listing for an item…, the item listing including an item description comprised of unstructured text;
processing the unstructured text …, to determine that the unstructured text includes a compatible item;
based on determining,… , that the unstructured text includes the compatible item, …, extracting from the unstructured text, a brand identifier and a model identifier for the compatible items included in the unstructured text,… to reduce a search space for identifier extraction;
utilizing the extracted model identifier and brand identifier, executing a search query for item listings having the brand identifier and the model identifier; and
providing, …, a bundle recommendation comprising the item listing for the item and the first compatible item listing.
This arrangement amounts to certain methods of organizing human activity associated with sales activities and commercial interactions involving receiving item listings, determining items that are compatible items from the unstructured text, searching for items based on identifiers, and providing a bundle recommendation comprising the item and compatible item. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A computer-implemented method …(Claim 1);
…from an item listing system ….at a listing platform …by a classification model of a machine learning engine… by the classification model… using a natural language processing model of the machine learning engine; wherein the classification model is executed prior to the natural language processing model …; …to a user device (Claims 1, 8, 15 );
the listing platform (Claims 2, 4, 5);
using one or more entity recognition natural language processing models (Claims 2 & 16);
the user device (Claim 7);
One or more non-transitory computer storage media storing computer- readable instructions that when executed by a processor, cause the processor to perform operations, the operations comprising: (Claim 8);
a generative pre-trained transformer (Claim 9);
entity recognition natural language processing (Claims 10 & 14);
A system comprising: at least one processor; and one or more computer storage media storing computer-readable instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising: (Claim 15).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A computer-implemented method …(Claim 1);
…from an item listing system ….at a listing platform …by a classification model of a machine learning engine… by the classification model… using a natural language processing model of the machine learning engine; wherein the classification model is executed prior to the natural language processing model …; …to a user device (Claims 1, 8, 15 );
the listing platform (Claims 2, 4, 5);
using one or more entity recognition natural language processing models (Claims 2 & 16);
the user device (Claim 7);
One or more non-transitory computer storage media storing computer- readable instructions that when executed by a processor, cause the processor to perform operations, the operations comprising: (Claim 8);
a generative pre-trained transformer (Claim 9);
entity recognition natural language processing (Claims 10 & 14);
A system comprising: at least one processor; and one or more computer storage media storing computer-readable instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising: (Claim 15).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 7-8, and 10-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (US Patent No. 9959563 B1, hereinafter “Wu”) in view of Balasubramanian et al. (US Pub. No. 20240220511 A1, hereinafter “Balasubramanian”).
Regarding claims 1, 8, 15
Wu discloses a computer-implemented method comprising:
accessing, from an item listing system, an item listing for an item at a listing platform, the item listing comprising an item description for the item comprised of unstructured text; (Wu, C4, L1-15: access items from electronic catalog system; C5, L10-50: obtain information about electronic catalog content including items available for purchase by user that includes item title and specifications, and user reviews; C6, L49-67: item related content includes free form text);
processing the unstructured text …to determine that unstructured text includes a compatible items (Wu, C7, L30-41: process for identifying attributes by shortening and filtering each word or subset of words and pattern matching to identify phrases as attributes; C2, L20-67: computer based mining processes for discovering associations between item attributes which include words to determine compatible items; C3, L1-10: );
based on determining,…., that the unstructured text includes the compatible item, using a natural language processing model…, extracting from the unstructured text, a brand identifier and a model identifier for the compatible item included in the unstructured text (Wu, C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L10-67: item-related content includes free-form text and attributes include information associated with the item (e.g. brand); C12, L25-45: attribute can include any type of attribute associated with an item (e.g. brand and model)),…;
utilizing the extracted model identifier and brand identifier, executing a search query for item listings having the brand identifier and the model identifier; and providing, to a user device, a bundle recommendation comprising results of the executed search query and the item listing for the item (Wu, C5, L3-30: search engine for searching catalog with items; C7, L9-55: attribute extractor extracts attributes by analyzing phrases included in the item-related content and extracted attributes are used to search for an item; C16, L1-26: recommendations based on multiple attributes; C12, L25-45: attribute can include any type of attribute associated with an item (e.g. brand and model); C8, L1-25: generate recommendations including a pool of items with multiple attributes; C20, L5-60: selects items to recommend to user based on attributes of the accessed item; C7, L55-67: generate recommendation based on the relation between different items based on the attributes of the items; C6, L49-67: item-related content includes free-form text; C9, L40-67: provide recommendation to the user based on generated recommendation rule and relationship and attributes of one item with another item (e.g. 3D glasses recommended with 3D TV; C10 , L19-45: item-to item association data repository used to generate recommendations; C19, L1-35: recommends one or more items from the subset of items on recommendation lists based on attributes; C4, L15-67: interactive computing system recommends items to user systems; C12, L25-45: attribute can include any type of attribute associated with an item (e.g. brand and model); C23, L55-65: user system includes computing devices).
Wu does not teach:
processing…by a classification model of a machine learning engine…(emphasis added);…based on determining, by the classification model, …, using a natural language processing model of the machine learning engine,…, wherein the classification model is executed prior to the natural language processing model to reduce a search space for identifier extraction; (emphasis added);
However, Balasubramanian teaches:
processing…by a classification model of a machine learning engine…(emphasis added);…based on determining, by the classification model, …, using a natural language processing model of the machine learning engine,…, wherein the classification model is executed prior to the natural language processing model to reduce a search space for identifier extraction; (emphasis added) (Balasubramanian, [0016]: machine learning used to identify data and enhance the natural language keywords; [0019]: using another machine learning model to extract and categorize data; FIG, 5, [0072]: in step 502, a machine learning model is used to identify terms; FIG. 5, [0073]: in step 503, first, a machine learning based code detector model is used, then a natural language processing technique is used; [0077]:narrowing the scope of the search);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the processing, determining, and natural language processing model of Wu with a classification model of a machine learning engine executed prior to a natural language processing model of the machine learning engine as taught by Balasubramanian because the results of such a modification would be predictable. Specifically, Wu would continue to teach processing, determining, and natural language processing model except that now a classification model of a machine learning engine executed prior to a natural language processing model of the machine learning engine is taught according to the teachings of Balasubramanian in order to enhance search technology. This is a predictable result of the combination. (Balasubramanian. [0013-0014]).
Regarding claim 2
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 1, further comprising: prior to selecting the item listing for the first compatible item, extracting, from the inventory of the listing platform, a plurality of item listings for a plurality of compatible items based on the identifiers included within item descriptions of each of the plurality of compatible items, the plurality of compatible items extracted using one or more entity recognition natural language processing models; and selecting the item listing for the first compatible item of the plurality of compatible items (Wu, FIG. 7, C20, L1-60: attribute extractor determines attributes 704 prior to determining set of items to recommend at step 706; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text).
Regarding claim 3
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 2, further comprising: determining the first compatible item based on user interactions, by other users, with bundle recommendations that each comprise at least one of the plurality of item listings (Wu, C10, L20-45: first item and second item recommended based on percentage of other users that purchased the first and second item together; C20, L40-60: selects items to recommend to user based on user-to-user relationships and purchase histories; C21, L15-30: recommend bestseller items; C8, L5-25: recommend based on users who view item).
Regarding claim 4
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 1, further comprising: identifying a first category for a first subset of the compatible items; identifying, from the inventory of the listing platform, a plurality of item listings for items of the first category based on one or more identifiers determined for the first subset of the compatible items; determining a ranking for each item listing from the plurality of item listings for the items of the first category; and wherein the item listing for the first compatible item is selected from the plurality of item listings for the items of the first category based on the rankings (Wu, C2, L35-55: ” an item category can be included as part of a facet. For example, in the above example, LED TV may be an item category and “3D” may be an attribute. The combination of the item category and the attribute may result in the facet {LED TV+3D}; C13, L45-67: Table 1, count of items in category 1; C14, L45-67: determine first count of items in category 1; C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C10, L20-45: first item and second item recommended based on percentage of other users that purchased the first and second item together).
Regarding claim 5
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 4, further comprising: identifying a second category for a second subset of the compatible items; identifying, from the inventory of the listing platform, a second plurality of item listings for items of the second category based on one or more identifiers determined for the second subset of the compatible items; determining a ranking for each item listing from the second plurality of item listings for the items of the second category; selecting an item listing for a second compatible item based on the ranking; and wherein the bundle recommendation further comprises the item listing for the second compatible item (Wu, C13, L45-67: Table 1, count of items in category 2; C14, L45-67: determine first count of items in category 2; C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C10, L20-45: first item and second item recommended based on percentage of other users that purchased the first and second item together).
Regarding claim 7
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 1, further comprising: mapping entities to the identifiers for the compatible items using a portion of each of the identifiers to determine an entity and an index comprising the item linked to the compatible items, each of the compatible items ordered within the index based on user interaction data; identifying the first compatible item and the entity for the first compatible item based on the mapping; and providing, to the user device, the bundle recommendation including the entity for the first compatible item (Wu, C2, L. 20-35: item facet is a set of item attributes and descriptors; C22, L49-67: determine user behavior based associations between facets of one item and facets of the second item based on a measure of how frequently the two items are purchased together; C6, L10-67: extract attributes from manufacturer descriptions of items such as company or author of item and features of item to identify keywords that may be used as attributes; C7, L9-27: attribute extractor extracts attributes by analyzing phrases filtering out single words; C11, L1-25: generating recommendations for candidate items purchased frequently together by using extracted item attributes and existing relationships between items; C19, L1-35: recommends one or more items from the subset of items on recommendation lists based on attributes).
Regarding claim 10
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 8, wherein the first compatible item is selected based on extracting the first identifier from a first item description within a first item listing of the first compatible item using entity recognition natural language processing (Wu, C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text)).
Regarding claim 11
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 8, further comprising: determining that the unstructured text of the item description includes a plurality of compatible items; identifying an identifier for each of the plurality of compatible items included within the unstructured text of the item description; identifying a compatible item listing for each of the plurality of compatible items; determining a ranking for each of the compatible item listings; and
selecting the first compatible item for the bundle recommendation based on the ranking for each of the compatible item listings (Wu, C13, L45-67: Table 1, count of items in category 2; C14, L45-67: determine first count of items in category 2; C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C10, L20-45: first item and second item recommended based on percentage of other users that purchased the first and second item together; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text).
Regarding claim 12
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 11, wherein each of the compatible item listings are ranked based on user feedback for each of the plurality of compatible items or shipment data for each of the plurality of compatible items (Wu, C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C8, L15-25: generate a rule that users who purchase a laptop computer that is associated with a price attribute of “<$500; C9, L40-67: provide recommendation to the user based on generated recommendation rule; C6, L45-67: user reviews; C10, L45-67: share a sales ranking (e.g., products on top ten sales list)).
Regarding claim 13
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 8, wherein the first identifier is a model identifier, the model identifier including computer-readable code that distinguishes one model as a compatible item with the item from other models that are incompatible (Wu, C12, L25-45: process extracts attributes including brand and model; C8, L40-65: negative rules to filter and prevent items with attribute from being recommended to user who views another item with different attribute; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content; C11, L1-25: generating recommendations for candidate items purchased frequently together by using extracted item attributes; C24, L35-62: each process implemented by device programmed with server code).
Regarding claim 14
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 8, wherein the first compatible item is selected based on extracting the first identifier from unstructured text within a first item listing of the first compatible item using entity recognition natural language processing (Wu, C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text));.
Regarding claim 16
The combination of Wu and Balasubramanian teaches the system of claim 15, further comprising selecting the first compatible item identified based on identifying the first identifier from a first item description of a first item listing of the first compatible item, wherein the first identifier is identified from the first item description using the one or more natural language processing models (Wu, C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text));.
Regarding claim 17
The combination of Wu and Balasubramanian teaches the system of claim 16, wherein the bundle recommendation comprises the first item listing of the first compatible item (Wu, C20, L40-60: selects items to recommend to user; C7, L55-67: generate recommendation based on the relation between different items based on the attributes of the items; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content; C6, L49-67: item-related content includes free-form text; C9, L40-67: provide recommendation to the user based on generated recommendation rule and relationship and attributes of one item with another item (e.g. 3D glasses recommended with 3D TV; C10 , L19-45: item-to item association data repository used to generate recommendations; C19, L1-35: recommends one or more items from the subset of items on recommendation lists based on attributes; C4, L15-67: interactive computing system recommends items to user systems; C23, L55-65: user system includes computing devices).
Regarding claim 18
The combination of Wu and Balasubramanian teaches the system of claim 15, wherein the at least one identifier comprises a model and brand identifier (Wu, C12, L25-45: attribute includes model and brand; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description).
Regarding claim 19
The combination of Wu and Balasubramanian teaches the system of claim 15, further comprising:
determining that the unstructured text of the item description includes a plurality of compatible items; identifying a model identifier for each of the plurality of compatible items included within the unstructured text of the item description; identifying a compatible item listing for each of the plurality of compatible items; determining a ranking for each compatible item listing, the ranking based on prior bundle purchases comprising the corresponding compatible item of the plurality of compatible items; and selecting the first compatible item for the bundle recommendation based on the ranking for each of the compatible item listings (Wu, C13, L45-67: Table 1, count of items in category 2; C14, L45-67: determine first count of items in category 2; C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C10, L20-45: first item and second item recommended based on percentage of other users that purchased the first and second item together; C7, L9-27: attribute extractor extracts attributes by analyzing phrases included in the item-related content and natural language processing for identifying attributes to extract from item-related content including item description; C11, L1-25: extracting item attributes and existing relationships between items (e.g. frequency of items being purchased together by a user; C6, L49-67: item-related content includes free-form text; C12, L25-45: attribute includes model and brand).
Claim(s) 6, 9, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu and Balasubramanian as applied to claim 1 above, and further in view of Raviv et al. (US Pub. No. 20200273079 A1, hereinafter “Raviv”).
Regarding claim 6
The combination of Wu and Balasubramanian teaches the computer-implemented method of claim 4, wherein the ranking for each item listing from the plurality of item listings for the items of the first category are based on user interactions with each item listing, …, a price associated with each item listing, and feedback data comprising ratings for each item (Wu, C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C8, L15-25: recommendation rule based on a price attribute; C9, L40-67: provide recommendation to the user based on generated recommendation rule; C20,L40-65: sales ranking data or other ranking for items).
Wu and Balasubramanian does not teach:
shipment data for each item listing.
However, Raviv teaches that it is known to include:
shipment data for each item listing (Raviv, [0122]: item shipment information used for ranking) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified ranking items of Wu and Balasubramanian with shipment data used for the ranking as taught by Raviv because the results of such a modification would be predictable. Specifically, Wu and Balasubramanian would continue to teach ranking items except that now shipment data is used for ranking according to the teachings of Raviv in order to analyze trends in data such as shipment data. This is a predictable result of the combination. (Raviv, [0002-0003]).
Regarding claim 9
The combination of Wu and Balasubramanian teaches the one or more non-transitory computer storage media of claim 8,… to identify a model identifier within the unstructured text of the item description, the model identifier comprising a sequence of characters within the unstructured text that distinguishes a model from other models (Wu, C12, L25-45: process extracts attributes including brand and model; C8, L40-65: negative rules to filter and prevent items with attribute from being recommended to user who views another item with different attribute; C7, L9-40: attribute extractor extracts attributes by analyzing phrases and term words included in the item-related content; C6, L49-67: item-related content includes free-form text).
Wu and Balasubramanian do not teach:
wherein the identifiers are identified using a generative pre-trained transformer.
However, Raviv teaches:
wherein the identifiers are identified using a generative pre-trained transformer (Raviv, [0011]: GPT and item categorization).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified identifying a model identifier of Wu and Balasubramanian with using a generative pre-trained transformer to perform the identifying as taught by Raviv because the results of such a modification would be predictable. Specifically, Wu and Balasubramanian would continue to teach identifying a model identifier except that now a generative pre-trained transformer is used to perform the identifying according to the teachings of Raviv in order to infer item categories. This is a predictable result of the combination. (Raviv, [0012]).
Regarding claim 20
The combination of Wu and Balasubramanian teaches the system of claim 19, wherein each ranking is further based on …data for each of the compatible item listings (Wu, C18, L55-67: identify item category and generate recommendation list based on ranking; C11, L35-45: ranking filter may be used to identify an item with the highest sales rank from among a number of items identified for recommendation for a candidate item 210 using one or more of the recommendation rules; C8, L15-25: generate a rule that users who purchase a laptop computer that is associated with a price attribute; C9, L40-67: provide recommendation to the user based on generated recommendation rule),
Wu and Balasubramanian do not teach:
shipment data…, the shipment data comprising an estimated date of arrival, a price of shipping, and an origin of manufacture.
However, Raviv teaches:
shipment data…, the shipment data comprising an estimated date of arrival, a price of shipping, and an origin of manufacture (Raviv, [0122]: item shipment information used for ranking, [0008]: message includes delivery notification with delivery date, price, and manufacturer information; [0010]: data obtained from messages include price) .
The motivation to combine Wu and Balasubramanian with Raviv is the same as set forth above in claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Bacharach et al. (US Pub. No. 20200364767 A1) related to recommending information based on grouping information and Acharyya et al. (US Pub. No. 20150088598 A1) related text and semantic analytics to infer characteristics of the user's online commercial behavior. The non-patent literature not relied upon is cited as Preference Based Recommendation System for Apparel E-Commerce Sites related to using user reviews and natural language processing methods for recommendations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688