DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 01/10/2025.
• Claims 1-20 are currently pending and have been examined.
Priority
The applicant’s claim for benefit of Provisional Patent Application Serial No. 63/627,313 filed 01/31/2024 has been received and acknowledged.
Information Disclosure Statement
Information Disclosure Statement received 01/10/2025 has been reviewed and considered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 8-9, 13, and 15-16 are objected to because of the following informalities:
With regards to claims 8-9, the limitation “The system of clause” appears to have a grammatical error. The limitation should likely read “The system of claim.”
With regards to claims 13 and 15-16, the limitation “The method of clause” appears to have a grammatical error. The limitation should likely read “The method of claim.”
Appropriate correction 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 without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-9 are directed to a machine, claims 10-16 are directed to a process, and claims 17-10 are directed to a manufacture. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
receive interaction data indicative of an interaction with an information item associated with an anchor item in a first category;
in accordance with a determination that the first category is associated with a plurality of themes of a second category, apply at least one type selection model to determine a set of item types associated with the plurality of themes of the second category;
generate an ordered list of recommended items of the second category based on the set of item types; and
in response to the interaction data, enable display of the ordered list of recommended items of the second category.
The above limitations recite the concept of providing recommendations for items in a second category based on themes. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, item recommendation is a marketing and sales activity. This is further illustrated in paragraph [0088] of the Specification, describing the invention relating item purchases. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the determinations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Independent claims 10 and 17 recite similar limitations as claim 1 and as such, claims 10 and 17 fall within the same identified grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the Alice/Mayo test, claims 1, 10, and 17 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 10, and 17 recite additional elements, such as a system, comprising: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions; a display; a client device; a computer-implemented method; and a non-transitory computer-readable storage medium, having instructions stored thereon, which when executed by one or more processors cause the processors to. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 10, and 17 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 10, and 17 merely recite a commonplace business method (i.e., providing recommendations for items in a second category based on themes) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 10, and 17 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 10, and 17 specifying that the abstract idea of providing recommendations for items in a second category based on themes is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 10, and 17 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 10, and 17 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 10, and 17 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 10, and 17, these claims recite additional elements, such as a system, comprising: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions; a display; a client device; a computer-implemented method; and a non-transitory computer-readable storage medium, having instructions stored thereon, which when executed by one or more processors cause the processors to. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 10, and 17 are manual processes, e.g., receiving information, analyzing information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1, 10, and 17 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims specifying that the abstract idea of providing recommendations for items in a second category based on themes is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 10, and 17 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 10, and 17 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-9, 11-16, and 18-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they recite an abstract idea, are not integrated into a practical application, and do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-9, 11-16, and 18-20 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they further recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors and managing personal behavior or relationships or interactions between people. These claims, under their broadest reasonable interpretation, further fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 3, 6-9, 12-14, and 18 fail to identify additional elements and as such, are not indicative of integration into a practical application. Dependent claims 2, 4-5, 11, 15-16, and 19-20 further identify additional elements, such as an item database, a large language model (LLM), a third-party server, an online shopping application, an Internet browser or a dedicated application, and a user interface. Similar to discussion above the with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). As such, under Step 2A, dependent claims 2-9, 11-16, and 18-20 are “directed to” an abstract idea. Similar to the discussion above with respect to claims 1, 10, and 17, dependent claims 2-9, 11-16, and 18-20 analyzed individually and as an ordered combination, invoke such additional elements as a tool to perform the abstract idea and merely indicate a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, and therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Accordingly, under the Alice/Mayo test, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 102
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 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. 102 that forms the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless —
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 10, 14, and 17-19 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Yang et al. (US 20200258137 A1), hereafter Yang.
In regards to claim 1, Yang discloses a system, comprising: a non-transitory memory having instructions stored thereon; and at least one processor operatively coupled to the non-transitory memory, and configured to read the instructions to (Yang: [0001]; [0004]):
receive interaction data indicative of an interaction with an information item associated with an anchor item in a first category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2”; [0002] – “receive a selection of a first item via a user interface”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; [0018] – “products or commodities in web-based platform 100 can be grouped into different categories, such as accessories, women clothes, women shoes, men clothes, men shoes, bags & handbags, jewelry, tea & food & supplements, watches, cameras & photo, mobile phones, computer & networking, electronics, sports goods, etc. In addition, the goods in the same major category can be further classified into different sub-categories. For example, the goods in the category for women clothes can be further divided into sub-categories such as down coats, short coats, one-piece, wedding dresses, fur, T-shirts, trousers, suits, jackets, sweaters, skirts, evening dresses, jeans”; the examiner notes Fig. 6 displays an item falling into categories such as shirt, fruit patterned, specific fabric, specific shape);
in accordance with a determination that the first category is associated with a plurality of themes of a second category, apply at least one type selection model to determine a set of item types associated with the plurality of themes of the second category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; the examiner notes the second category is scent relevant items (i.e., perfume, cologne, etc.));
generate an ordered list of recommended items of the second category based on the set of item types (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; the examiner notes Fig. 6 displays the perfumes in an ordered list); and
in response to the interaction data, enable display of the ordered list of recommended items of the second category on a display of a client device (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; the examiner notes Fig. 6 displays the perfumes in an ordered list…content in product page 600 includes scent-irrelevant item BP2 and the selected set of scent-relevant items AP1-AP4 and is displayed in the manner associated with the determined theme data”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”).
In regards to claim 2, Yang discloses the system of claim 1. Yang further discloses wherein the at least one type selection model includes a feature extraction model, and the instructions to determine the set of item types associated with the plurality of themes of the second category further comprise instructions to: extract, from an item database, information of a plurality of items (Yang: [0036] – “the electronic nose is able to identify and capture odor information, which is recorded and transformed into digital values. The digital values can be further computed and analyzed based on various statistical models, in order to obtain the scent data, e.g., scent data Scent(AP1), associated with the scent-relevant item, e.g., item AP1. Accordingly, database 242 can store a first relationship including the scent-relevant items and their scent data”; [0038] – “apparatus 200 can apply a scent-emotional model (e.g., model 244 in FIG. 2) to identify emotions or themes related to the scent. Thus, theme data, e.g., Theme(AP1), associated with a scent-relevant item, e.g., item AP1, can be determined using the scent-emotional model”; [0042] – “These Emotion and Odor Scale can be applied as the scent-emotional model to link one or more theme labels with the scent data of a selected item to obtain the theme data corresponding to the selected item and store the theme data in the corresponding entry. Further, while existed scent-emotional scale and sensory evaluation methods can be applied to build theme label for various scent-relevant and scent-irrelevant products, machine learning technology may also be applied to improve the accuracy and efficiency of identifying theme labels for items in a large scale); and
apply the feature extraction model to identify a plurality of item types of the second category based on the information of the plurality of items, wherein the plurality of item types includes the set of item types associated with the plurality of themes of the second category ([0038] – “apparatus 200 can apply a scent-emotional model (e.g., model 244 in FIG. 2) to identify emotions or themes related to the scent. Thus, theme data, e.g., Theme(AP1), associated with a scent-relevant item, e.g., item AP1, can be determined using the scent-emotional model”; [0042] – “These Emotion and Odor Scale can be applied as the scent-emotional model to link one or more theme labels with the scent data of a selected item to obtain the theme data corresponding to the selected item and store the theme data in the corresponding entry. Further, while existed scent-emotional scale and sensory evaluation methods can be applied to build theme label for various scent-relevant and scent-irrelevant products, machine learning technology may also be applied to improve the accuracy and efficiency of identifying theme labels for items in a large scale”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’).
In regards to claim 10, claim 10 is directed to a method. Claim 10 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Yang discloses the limitations of claim 1 as noted above. Yang further discloses a computer-implemented method (Yang: [0003]; [0069]). Claim 10 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 14, Yang discloses the method of claim 10. Yang further discloses determining that the first category is associated with the plurality of themes of the second category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0066] and Fig. 7 – “an exemplary scenario of a customer browsing between webpage 500 of online store CA and product page 600”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”).
In regards to claim 17, claim 17 is directed to a medium. Claim 10 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards a system. The system of Yang discloses the limitations of claim 1 as noted above. Yang further discloses a non-transitory computer-readable storage medium, having instructions stored thereon, which when executed by one or more processors cause the processors to (Yang: [0003]; [0026]). Claim 17 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claim 18, Yang discloses the medium of claim 17. Yang further discloses wherein the interaction data includes one or both of: a selection of the information item to add the anchor item of the first category into a shopping basket; and a selection of the information item to open a page of the anchor item of the first category for further review (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2”; [0002] – “receive a selection of a first item via a user interface”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; [0018] – “products or commodities in web-based platform 100 can be grouped into different categories, such as accessories, women clothes, women shoes, men clothes, men shoes, bags & handbags, jewelry, tea & food & supplements, watches, cameras & photo, mobile phones, computer & networking, electronics, sports goods, etc. In addition, the goods in the same major category can be further classified into different sub-categories. For example, the goods in the category for women clothes can be further divided into sub-categories such as down coats, short coats, one-piece, wedding dresses, fur, T-shirts, trousers, suits, jackets, sweaters, skirts, evening dresses, jeans”).
In regards to claim 19, Yang discloses the medium of claim 17. Yang further discloses further comprising instructions to: execute an online shopping application via an Internet browser or a dedicated application, wherein the interaction data indicative of the interaction with on the information item is received via a user interface of the online shopping application, and the ordered list of recommended items are displayed on the user interface of the online shopping application (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0066] and Fig. 7 – “an exemplary scenario of a customer browsing between webpage 500 of online store CA and product page 600”; [0024] – “Web-based platform 100 can generate contents to be displayed on a user interface (e.g., a shopping page on a website or in a mobile application)”; [0030] – “Apparatus 200 can transmit data to and communicate with other servers and receive responses from client terminals through network 250. Network 250 may be a local network, an internet service provider, internet”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; [0018] – “products or commodities in web-based platform 100 can be grouped into different categories, such as accessories, women clothes, women shoes, men clothes, men shoes, bags & handbags, jewelry, tea & food & supplements, watches, cameras & photo, mobile phones, computer & networking, electronics, sports goods, etc. In addition, the goods in the same major category can be further classified into different sub-categories. For example, the goods in the category for women clothes can be further divided into sub-categories such as down coats, short coats, one-piece, wedding dresses, fur, T-shirts, trousers, suits, jackets, sweaters, skirts, evening dresses, jeans”).
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, in view of Arora et al. (US 20210241343 A1), hereinafter Arora.
In regards to claim 3, Yang discloses the system of claim 2. Yang further discloses wherein: the instructions to apply the feature extraction model further comprises instructions to determine a query associated with a first query for identifying item types in the second category that is related to the first category and instructions to determine a plurality of item type of a collection of item types based on the information of the plurality of items (Yang: [0063] and Fig. 4 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0061] – “when a customer enters online store CA and selects one product to view details of the product, webpage 500 of online store CA displays visual codes ID1-ID4 and storyboard 514 using scent as a clue”; the request/selection of the initial product is interpreted to be a query); and
the instructions to determine the set of item types further comprises instructions to: determine a respective similarity level between each of the plurality of item type with the query associated with the second category; and based on the respective similarity levels, select the plurality of item types corresponding to the highest similarity levels among the collection of item types (Yang: [0063] and Fig. 4 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”).
Yang further discloses a vector analysis (Yang: [0037]), yet Yang does not explicitly disclose that a query is a query embedding and that item types are item type embeddings.
However, Arora teaches a similar recommendation system (Arora: [abstract]), including
that a query is a query embedding and that item types are item type embeddings (Arora: [0075] – “the two sets of trained item embeddings”; [0101-0102] – “The inference phase of the triple embeddings model (e.g., triple2vec) can be transformed into a similarity search of dense embedding vectors… The first term in the argmax on the right side, [p.sub.i p.sub.i q.sub.i q.sub.i].sup.T, is the ANN index, as it only depends on i. The second term in the argmax on the right side, [q.sub.j h.sub.u p.sub.j h.sub.u], is based on inputs u and j, and is the query vector”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the embeddings of Arora in the system of Yang because Yang already discloses a vector analysis and Arora is merely demonstrating what the analysis may be. Additionally, it would have been obvious to have included that a query is a query embedding and that item types are item type embeddings as taught by Arora because embeddings are well-known and the use of it in a recommendation system would have resulted in more relevant recommendations (Arora: [0131]).
In regards to claim 7, Yang discloses the system of claim 1. Yang further discloses wherein the instructions to determine the set of item types associated with the plurality of themes of the second category further comprise instructions to: apply the at least one or more type selection models to identify a subset of item types of the second category based on the plurality of themes of the second category; and in accordance with a criterion, select the set of item types from the subset of item types based on correlation information of the first and second categories (Yang: [0063] and Fig. 4 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”).
Yang further discloses online shopping (Yang: [0015]), yet Yang does not explicitly disclose correlation information determined based on historic transaction data.
However, Arora teaches a similar recommendation system (Arora: [abstract]), including
correlation information determined based on historic transaction data (Arora: [0083] – “the triple embeddings model can be trained as describe above, based on past purchase history, using triplets of (user, first item, second item), in which the first item and the second item were selected (e.g., purchased) in the same basket by the user”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included historic data of Arora in the system of Yang because Yang already discloses data and Arora is merely demonstrating what the data may be. Additionally, it would have been obvious to have included correlation information determined based on historic transaction data as taught by Arora because historic information is well-known and the use of it in a recommendation system would have resulted in more relevant recommendations (Arora: [0131]).
Claims 4-6, 11, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, in view of Wang et al. (US 20240241897 A1), hereinafter Wang.
In regards to claim 4, Yang discloses the system of claim 2. Yang further discloses wherein the at least one type selection model includes a model, and the instructions to determine the set of item types associated with the plurality of themes of the second category further comprise instructions to: apply at least to select a subset of item types from the plurality of item types based on the plurality of themes of the second category, wherein the subset of item types includes the set of item types associated with the plurality of themes of the second category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose that a model includes a large language model (LLM), and apply at least the LLM.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
that a model includes a large language model (LLM), and apply at least the LLM (Wang: [0043] – “the LLM is able to perform various tasks and synthesize and formulate output responses based on information extracted from the training data”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the LLM of Wang in the system of Yang because Yang already discloses a model and Wang is merely demonstrating what the model may be. Additionally, it would have been obvious to have included that a model includes a large language model (LLM), and apply at least the LLM as taught by Wang because LLMs are well-known and the use of it in a recommendation system would have provided a text-to-text interface, eliminating a need for a separate query understanding system (Wang: [0049]).
In regards to claim 5, Yang/Wang discloses the system of claim 4. Yang further discloses the system further comprising instructions to: send a query for the subset of item types to the server, the query including one or more of: the plurality of themes, a number of item types to be selected for each theme, and a list of the identified plurality of item types; and receive, from the server, a response including the subset of item types associated with the plurality of themes of the second category (Yang: [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”; [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; [0025] and Fig. 2 – “apparatus 200 may be a server to implement the recommendation system”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose wherein the LLM is provided by a third-party server; and that a server is the third-party server.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
wherein the LLM is provided by a third-party server; and that a server is the third-party server (Wang: [0035] – “AI system 125 may be a third-party server that is independent and separate from the online concierge system 140”; [0063] – “the search and recommendation model 215 may e.g., provide the generated prompts to the third party system. The third party system applies the prompts to the model to generate the one or more item predictions, and provides the generated one or more item predictions to the online concierge system 140.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Wang with Yang for the reasons identified above with respect to claim 4.
In regards to claim 6, Yang/Wang discloses the system of claim 4. Yang further discloses wherein the instructions to determine the set of item types associated with the plurality of themes of the second category further comprise instructions to: in accordance with a lift criterion, select the set of item types from the subset of item types based on correlation information of the first and second categories (Yang: [0063] and Fig. 4 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”).
In regards to claim 11, Yang discloses the method of claim 10. Yang further discloses determine that the first category is associated with the plurality of themes of the second category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose applying an LLM.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
applying an LLM (Wang: [0043] – “the LLM is able to perform various tasks and synthesize and formulate output responses based on information extracted from the training data”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the LLM of Wang in the system of Yang because Yang already discloses a model and Wang is merely demonstrating what the model may be. Additionally, it would have been obvious to have included applying an LLM as taught by Wang because LLMs are well-known and the use of it in a recommendation system would have provided a text-to-text interface, eliminating a need for a separate query understanding system (Wang: [0049]).
In regards to claim 15, Yang discloses the method of claim 14. Yang further discloses determine the plurality of themes of the second category associated with the first category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose wherein a large language model (LLM) is applied to determine.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
wherein a large language model (LLM) is applied to determine (Wang: [0043] – “the LLM is able to perform various tasks and synthesize and formulate output responses based on information extracted from the training data”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the LLM of Wang in the system of Yang because Yang already discloses a model and Wang is merely demonstrating what the model may be. Additionally, it would have been obvious to have included wherein a large language model (LLM) is applied to determine as taught by Wang because LLMs are well-known and the use of it in a recommendation system would have provided a text-to-text interface, eliminating a need for a separate query understanding system (Wang: [0049]).
In regards to claim 16, Yang/Wang discloses the method of claim 15. Yang further discloses sending a prompt for the plurality of themes of the second category to the server, the prompt including information of the first category and information of the second category; and receiving, from the server, a response including the plurality of themes of the second category (Yang: [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”; [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”; [0025] and Fig. 2 – “apparatus 200 may be a server to implement the recommendation system”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose wherein the LLM is provided by a third-party server; and that a server is the third-party server.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
wherein the LLM is provided by a third-party server; and that a server is the third-party server (Wang: [0035] – “AI system 125 may be a third-party server that is independent and separate from the online concierge system 140”; [0063] – “the search and recommendation model 215 may e.g., provide the generated prompts to the third party system. The third party system applies the prompts to the model to generate the one or more item predictions, and provides the generated one or more item predictions to the online concierge system 140.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Wang with Yang for the reasons identified above with respect to claim 15.
In regards to claim 20, Yang discloses the medium of claim 17. Yang further discloses determine that the first category is associated with the plurality of themes of the second category (Yang: [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data”; [0057] and Figs. 5-6 – “Content description 512 of the theme can be determined based on the associated theme label(s) of the theme. For example, the theme of recommendation board 510 may be ‘Lively,’ ‘Fresh,’ and ‘Flower/Fruit.’ The storyboard 514 shows items (e.g., items BP2, CP3, DP4, EP5, and FP6) that are consistent with the theme, e. g., sharing the same or similar theme, such as similar spiritual or emotional attributes associated with the scent of items AP1-AP4. For example, the items displayed in storyboard 514 may include associated sceneries, fashion goods, foods, furniture, and household items, etc.”; [0019] – “scent-relevant item, such as foods, perfumes, air fresheners or home scents, cosmetics, bath and body products, deodorants, cleaning supplies, candles, or any other scent-oriented products”; [0020-0022] – “link scent-irrelevant items BP2, CP3, DP4, EP5, and FP6 in different categories to scent-relevant item AP1 based on their thematic attributes such as ‘emotional attributes’…for scent-irrelevant items, the emotional attributes can be associated with other characteristics, such as colors, shapes, designs, fabrics, functions, etc. For example, flower patterns, floral backgrounds, pink colors, or smooth fabrics may link to a ‘romantic’ emotional theme. Bright yellow colors or sportswear fabric may link to an ‘energetic’ emotional theme”).
Yang further discloses using machine learning to identify themes (Yang: [0042]), yet Yang does not explicitly disclose apply an LLM to determine.
However, Wang teaches a similar recommendation system (Wang: [abstract]), including
apply an LLM to determine (Wang: [0043] – “the LLM is able to perform various tasks and synthesize and formulate output responses based on information extracted from the training data”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the LLM of Wang in the system of Yang because Yang already discloses a model and Wang is merely demonstrating what the model may be. Additionally, it would have been obvious to have included apply an LLM to determine as taught by Wang because LLMs are well-known and the use of it in a recommendation system would have provided a text-to-text interface, eliminating a need for a separate query understanding system (Wang: [0049]).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over, in view of Arora, in view of B (US 20180174164 A1), hereinafter B.
In regards to claim 8, Yang/Arora discloses the system of claim 7. Yang further discloses wherein the instructions to select the set of item types from the subset of item types further comprise instructions to: for each of the subset of item types, determine the correlation information of the first and second categories including a lift parameter; identify a preliminary set of item types from the subset of item types, in accordance with that a determination; and wherein the set of item types include a predefined number of item types that have the largest lift parameters among the preliminary set of item types (Yang: [0063] and Fig. 4 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; [0053] – “the apparatus selects a set of second items…using the determined theme data. In some embodiments, the apparatus can perform a search in the database, and select the items with the theme data consistent with selected first item. For example, the apparatus can select one candidate item in the set of second items, if the theme data of the selected candidate item has one or more identical theme labels. In another example, the apparatus can evaluate a similarity between the theme data of one candidate item and the determined theme data of the selected first item. If the similarity is greater than a threshold value, the apparatus can select the candidate item in the set of second item”).
Yang further discloses a parameter of each of the preliminary set of item types and (2) the score of each of the preliminary set of item types (Yang: [0053]), yet Yang does not explicitly disclose the correlation information including a support parameter, a confidence score, and a determination that (1) the support parameter is greater than a first threshold and (2) the confidence score is greater than a second threshold.
However, Arora teaches a similar recommendation system (Arora: [abstract]), including
the correlation information including a support parameter (Arora: [0054] – “the complementary category filtering technique can involve calculating support and lift metrics”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the parameter of Arora in the system of Yang because Yang already discloses a parameter and Arora is merely demonstrating what the parameter may be. Additionally, it would have been obvious to have included the correlation information including a support parameter as taught by Arora because support parameters are well-known and the use of it in a recommendation system would have resulted in more relevant recommendations (Arora: [0131]).
Additionally, B teaches a similar transactional data system (B: [0001]), including
the correlation information including a confidence score, and a determination that (1) the support parameter is greater than a first threshold and (2) the confidence score is greater than a second threshold (B: [0011] – “Default statistical parameters can include a minimum support, a minimum confidence”; [0090] – “support index, confidence index and average lift index. Support, confidence and lift are parameters used in predictive analysis to judge the viability of a particular item and its association with other items”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the score and thresholds of B in the system of Yang because Yang already discloses a parameter and threshold and B is merely demonstrating what the parameter and threshold may be. Additionally, it would have been obvious to have included the correlation information including a confidence score, and a determination that (1) the support parameter is greater than a first threshold and (2) the confidence score is greater than a second threshold as taught by B because lift analysis is well-known and the use of it in a recommendation system would have judged the viability of an item (B: [0090]).
In regards to claim 9, Yang/Arora/B discloses the system of claim 8.
Yet Yang does not explicitly disclose instructions to, for each of the subset of item types (ET):determine the support parameter (S) based on a ratio of a number N(CT,ET) of transactions including at least one item of both the first category and at least one item of the respective item type of the second category in the historic transaction data and a total number NTx of transactions in the historic transaction data; determine the confidence score (C) based on a ratio of the number N(CT,ET) of transactions and a number N(CT) of transactions including one or more items of the first category in the historic transaction data; and determine the lift parameter (L) based on a ratio of the number N(CT,ET) of transactions and a product of the number N(CT) of transactions and a number N(ET) of transactions including one or more items of the respective type of the second category in the historic transaction data.
However, Arora teaches a similar recommendation system (Arora: [abstract]), including
instructions to, for each of the subset of item types (ET):determine the support parameter (S) based on a ratio of a number N(CT,ET) of transactions including at least one item of both the first category and at least one item of the respective item type of the second category in the historic transaction data and a total number NTx of transactions in the historic transaction data (Arora: [0054] – “In several embodiments, the complementary category filtering technique can involve calculating support and lift metrics, as follows: Support(A)=fraction of all transactions that contain the item A”); and
determine the lift parameter (L) based on a ratio of the number N(CT,ET) of transactions and a product of the number N(CT) of transactions and a number N(ET) of transactions including one or more items of the respective type of the second category in the historic transaction data (Arora: [0054] – “In several embodiments, the complementary category filtering technique can involve calculating support and lift metrics, as follows: …Lift(A.fwdarw.B)=Support(A.Math.B)/(Support(A)×Support(B)) where A is a given anchor item, and B is an item to be recommended from anchor item A, denoted as (A.fwdarw.B). When B is popular item, but unrelated to anchor item A, the lift metric will be low. When B is complementary to A, but not merely popularly co-bought, the lift metric will be high”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the ratios of Arora in the system of Yang because Yang already discloses an analysis and Arora is merely demonstrating what the analysis may be. Additionally, it would have been obvious to have included instructions to, for each of the subset of item types (ET): determine the support parameter (S) based on a ratio of a number N(CT,ET) of transactions including at least one item of both the first category and at least one item of the respective item type of the second category in the historic transaction data and a total number NTx of transactions in the historic transaction data; and determine the lift parameter (L) based on a ratio of the number N(CT,ET) of transactions and a product of the number N(CT) of transactions and a number N(ET) of transactions including one or more items of the respective type of the second category in the historic transaction data as taught by Arora because ratios are well-known and the use of it in a recommendation system would have resulted in more relevant recommendations (Arora: [0131]).
Additionally, B teaches a similar transactional data system (B: [0001]), including
determine the confidence score (C) based on a ratio of the number N(CT,ET) of transactions and a number N(CT) of transactions including one or more items of the first category in the historic transaction data (B: [0090] – “Confidence is the ratio of the number of transactions that include all items in the consequent, as well as the antecedent (the support) to the number of transactions that include all items in the antecedent”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the confidence calculation in the system of Yang because Yang already discloses an analysis and B is merely demonstrating what the analysis may be. Additionally, it would have been obvious to have included determine the confidence score (C) based on a ratio of the number N(CT,ET) of transactions and a number N(CT) of transactions including one or more items of the first category in the historic transaction data as taught by B because confidence is well-known and the use of it in a recommendation system would have judged the viability of an item (B: [0090]).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Yang, in view of Pope et al. (US 20150339759 A1), hereinafter Pope.
In regards to claim 12, Yang discloses the method of claim 10. Yang further discloses wherein generating the ordered list of recommended items of the second category based on the set of item types further comprises, for each of the set of item types: selecting a respective set of distinct items of the respective type based on information; and the selected distinct items of the set of item types based on the information (Yang: [0036] – “the electronic nose is able to identify and capture odor information, which is recorded and transformed into digital values. The digital values can be further computed and analyzed based on various statistical models, in order to obtain the scent data, e.g., scent data Scent; and ranking the selected distinct items of the set of item types based on the trending information (Yang: [0036] – “the electronic nose is able to identify and capture odor information, which is recorded and transformed into digital values. The digital values can be further computed and analyzed based on various statistical models, in order to obtain the scent data, e.g., scent data Scent(AP1), associated with the scent-relevant item, e.g., item AP1. Accordingly, database 242 can store a first relationship including the scent-relevant items and their scent data”; [0063] and Fig. 6 – “apparatus 200 can receive a selection of a scent-irrelevant item, such as item BP2, determine the theme data corresponding to the selected scent-irrelevant item, select a set of scent-relevant items (such as items AP1-AP4) using the determined theme data, and generate the content to be displayed in the user interface”; the examiner notes Fig. 6 displays the perfumes in an ordered list…content in product page 600 includes scent-irrelevant item BP2 and the selected set of scent-relevant items AP1-AP4 and is displayed in the manner associated with the determined theme data”; [0038] – “apparatus 200 can apply a scent-emotional model (e.g., model 244 in FIG. 2) to identify emotions or themes related to the scent. Thus, theme data, e.g., Theme(AP1), associated with a scent-relevant item, e.g., item AP1, can be determined using the scent-emotional model”; [0042] – “These Emotion and Odor Scale can be applied as the scent-emotional model to link one or more theme labels with the scent data of a selected item to obtain the theme data corresponding to the selected item and store the theme data in the corresponding entry. Further, while existed scent-emotional scale and sensory evaluation methods can be applied to build theme label for various scent-relevant and scent-irrelevant products, machine learning technology may also be applied to improve the accuracy and efficiency of identifying theme labels for items in a large scale).
Yet Yang does not explicitly disclose the information is trending information, and ranking the items based on the trending information.
However, Pope teaches a similar recommendation system (Pope: [0080]), including
the information is trending information, and ranking the items based on the trending information (Pope: [0065] – “the first subset of products are associated with a best-selling list or any other type of product ranking. For example, the first subset of products may include the ten best-selling products by, for example, volume during the first time period”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the ranking of Pope in the system of Yang because Yang already discloses an analysis and Pope is merely demonstrating what the analysis may be. Additionally, it would have been obvious to have included the information is trending information, and ranking the items based on the trending information as taught by Pope because rankings are well-known and the use of it in a recommendation system would have resulted in better new product recommendations (Pope: [0017]).
In regards to claim 13, Yang/Pope discloses the method of claim 15. Yet Yang does not explicitly disclose wherein a sum of respective numbers of selected items of the set of item types is equal to a predefined number, and the trending information includes a number of items sold for a predefined length of sales history for each of the respective set of distinct items.
However, Pope teaches a similar recommendation system (Pope: [0080]), including
wherein a sum of respective numbers of selected items of the set of item types is equal to a predefined number, and the trending information includes a number of items sold for a predefined length of sales history for each of the respective set of distinct items (Pope: [0065] – “the first subset of products are associated with a best-selling list or any other type of product ranking. For example, the first subset of products may include the ten best-selling products by, for example, volume during the first time period”; [0046] – “products that share a sales ranking (e.g., products on top ten sales list by volume and/or by monetary sales numbers)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Pope with Yang for the reasons identified above with respect to claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference U teaches an item recommendation method for cross-selling. Cross-category items may be recommended to a user. Items which are bought together may be highlighted for a user.
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/ANNA MAE MITROS/Examiner, Art Unit 3689
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