DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/954,737 filed on 11/21/2024. Claims 1-13 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/954,737 filed 11/21/2024 claims foreign priority to 2023-216709, filed 12/22/2023.
Response to Amendment
In the response filed April 13, 2026, Applicant amended claims 1-4 and 7-13, and did not cancel any claims. No new claims were presented for examination.
Applicant's amendments to claims 1, 7, and 8 are hereby acknowledged. The previous Claim Interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph has been removed in light of the claim amendments.
Applicant's amendments to claims 1, 7, and 12 are hereby acknowledged. The amendments are sufficient to overcome the previously issued rejection of claims 1-12 under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
Applicant's amendments to claim 13 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b) [Item 13, Office action dated 01/23/2026]; accordingly, this rejection has been maintained.
Applicant's amendments to claims 1, 12, and 13 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed April 13, 2026, have been fully considered.
Applicant submits “that the claims, as amended, are not directed to an abstract idea. The claims recite a specific technical solution to a technical problem in e-commerce systems. As described in the specification, "[i]f a plurality of stores at an e-commerce mall sell the same product, this product may be displayed and sold on different sales pages of the respective stores. If the same product is sold on different sales pages, the action information may not be appropriately associated with the product identification information." As-Filed Specification, paragraph [0006]. This results in user action information not being properly associated with product identification information, which in turn prevents appropriate determination of sales pages for products to be recommended to users. The amended claims solve this technical problem using specific technical data structures and operations. Specifically, the claims now recite converting the list of sales page identification information into a list of product identification information "using a product conversion table that associates each product identification information with one or more pieces of sales page identification information." See As-Filed Specification, paragraph [0022] ("The product conversion table 211 is a table that associates a product ID, which identifies a product being sold in the e-commerce mall, with sales page IDs, which identifies one or more sales pages that sell the product.").” [Applicant’s Remarks, 04/13/2026, pages 8-9]
The Examiner respectfully disagrees. In response to Applicant’s argument that “the claims recite a specific technical solution to a technical problem in e-commerce systems,” it is noted that while the Specification describes associating product IDs with sales page IDs using a conversion table, claim 1 does not recite any improvement to the computer itself or its underlying technical operation. The claimed steps related to acquiring, converting, generating recommendation, and determining a sales ae are generic data processing functions applied to e-commerce information. The use of a product conversion table constitutes an abstract organizational scheme or data arrangement. For the reasons above, this argument is found unpersuasive.
Applicant submits “This product conversion table is a specific technical data structure that stores associations between product IDs and sales page IDs-this is not something that can be performed mentally or with pen and paper.” [Applicant’s Remarks, 04/13/2026, page 9]
The Examiner respectfully disagrees. In response to Applicant’s argument that “This product conversion table is a specific technical data structure that stores associations between product IDs and sales page IDs-this is not something that can be performed mentally or with pen and paper,” it is noted that using a table to associate product IDs with sale pages IDs does not improve the computer itself or its underlying operation. Such an association is an abstract organizational concept. A person can, using pen and paper, list each product and its corresponding sales pages, then determine which page to recommend based on rules. Implementing the table in a computer merely automates this process. For the reasons above, this argument is found unpersuasive.
Applicant submits “Additionally, the amended claims now recite converting product identification information into a user vector representation using a machine learning model-this is an inherently computer-implemented operation that cannot be performed mentally. The claims further recite determining recommendations based on cosine similarity calculations in a common vector space where vector representations are embedded-these are technical operations performed by a computer. See As-Filed Specification, paragraph [0045].” [Applicant’s Remarks, 04/13/2026, page 9]
The Examiner respectfully disagrees. In response to Applicant’s argument that “the amended claims now recite converting product identification information into a user vector representation using a machine learning model-this is an inherently computer-implemented operation that cannot be performed mentally,” it is noted that converting product IDs into a vector representation and computing cosine similarity are mathematical operations. Although the claim requires a computer to perform these operations, using a generic processor to perform calculations does not transform the underlying abstract idea into patent eligible subject matter. For the reasons above, this argument is found unpersuasive.
Applicant submits “the amended claims recite determining "a unique sales page" for each recommended product, "wherein when a plurality of sales pages are specified for a product to be recommended, the unique sales page is determined out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages." The evaluation index, which may include a conversion rate (CVR) or evaluation scores as described in the specification, is a technical metric used to select among multiple sales pages. See As-Filed Specification, paragraph [0026]. These technical elements integrate any abstract idea into a practical application by improving e-commerce recommendation technology.” [Applicant’s Remarks, 04/13/2026, page 9]
The Examiner respectfully disagrees. In response to Applicant’s argument that “these technical elements integrate any abstract idea into a practical application by improving e-commerce recommendation technology,” it is noted that the additional elements in amended claim 1 are: at least one memory storing instructions, at least one processor, acquiring code configured to, converting code configured to, generating code configured to, a machine learning model, and determining code configured to, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
It is also noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no memory, processor, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to a unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages, which is not a technical result or improvement thereof.
Furthermore, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For the reasons above, this argument is found unpersuasive.
Applicant submits “ Similar to the claims found eligible in Core Wireless Licensing S.A.R.L. V. LG Electronics, Inc., 880 F.3d 1356, 1362-63 (Fed. Cir. 2018), which were directed to an improved user interface that displayed an application summary of unlaunched applications with selectable data, the amended claims here recite a specific technical improvement to how information is processed and displayed to users in an e-commerce interface.” [Applicant’s Remarks, 04/13/2026, pages 9-10]
In response to the Applicant’s arguments that “similar to the claims found eligible in Core Wireless Licensing S.A.R.L. V. LG Electronics, Inc., 880 F.3d 1356, 1362-63 (Fed. Cir. 2018), which were directed to an improved user interface that displayed an application summary of unlaunched applications with selectable data, the amended claims here recite a specific technical improvement to how information is processed and displayed to users in an e-commerce interface,” the Examiner respectfully disagrees. With respect to Applicant's comparison to Core Wireless, Examiner first notes that claim 1 does not recite nay graphical user interface or display element. The Examiner further points out that the claims in Core Wireless involved an improved user interface for computing devices, which was not an abstract idea. While the generic idea of summarizing information was known, the claims were directed to a “particular manner of summarizing and presenting information in electronic devices” that improved the efficiency of the electronic device over the prior art. As stated in Core Wireless, the claim recites a computing device comprising a display screen, the computing device being configured to display on the screen a menu listing one or more applications, and additionally being configured to display on the screen an application summary that can be reached directly from the menu, wherein the application summary displays a limited list of data offered within the one or more applications, each of the data in the list being selectable to launch the respective application and enable the selected data to be seen within the respective application, and wherein the application summary is displayed while the one or more applications are in an un-launched state. The claims at issue are far different from the claims in Core Wireless. The claims of the present case involve a method for determining the unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages. The claims of the instant application are not directed to an improved user interface for computing devices, as the claims in Core Wireless recite. Here, the claim merely provides, for an information processing apparatus preforming data acquisition, conversion, and recommendation functions. In the claims/specification here for example, there is no improved interface, improved navigation between menus, or ability to see statuses in un-launched states, as in Core Wireless. Accordingly, this argument is found unpersuasive.
Applicant submits “Amended claim 1 recites, inter alia, "converting code configured to cause at least one of the at least one processor to convert the list of sales page identification information into a list of product identification information identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information." Applicant submits that the cited prior art does not teach or suggest this limitation.” [Applicant’s Remarks, 04/13/2026, page 10]
In response to the Applicant’s argument that “Amended claim 1 recites, inter alia, "converting code configured to cause at least one of the at least one processor to convert the list of sales page identification information into a list of product identification information identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information." Applicant submits that the cited prior art does not teach or suggest this limitation,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 04/13/2026, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the new ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits Stoppelman's normalization process standardizes product names across retailers, but does not involve a data structure that associates product IDs with multiple sales page IDs as recited in the amended claims.” [Applicant’s Remarks, 04/13/2026, page 11]
As best understood by Examiner, Applicant argues that Stoppelman does not teach “using a product conversion table that associates each product identification information with one or more pieces of sales page identification information.” In response, the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 04/13/2026, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the new ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “Shotaro teaches recommending stores to users based on correlation between user queries and products the store provides. See Shotaro, Abstract. However, Shotaro does not teach or suggest a product conversion table that associates each product identification information with one or more pieces of sales page identification information.” [Applicant’s Remarks, 04/13/2026, page 11]
In response to Applicant’s argument that “Shotaro does not teach or suggest a product conversion table that associates each product identification information with one or more pieces of sales page identification information,” the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 04/13/2026, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the new ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “Furthermore, the amended claims now recite generating the recommended product list by converting product identification information into a user vector representation using a machine learning model and determining recommendations based on cosine similarity in a common vector space. Neither Stoppelman nor Shotaro teaches this specific combination of features.” [Applicant’s Remarks, 04/13/2026, page 11]
In response to the Applicant’s argument that “the amended claims now recite generating the recommended product list by converting product identification information into a user vector representation using a machine learning model and determining recommendations based on cosine similarity in a common vector space. Neither Stoppelman nor Shotaro teaches this specific combination of features,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 04/13/2026, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the new ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “Stoppelman teaches displaying product recommendations on a current product page being viewed by a user, not determining which sales page should be associated with each recommended product when multiple sales pages exist for the same product.” [Applicant’s Remarks, 04/13/2026, page 11]
In response to Applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., determining which sales page should be associated with each recommended product when multiple sales pages exist for the same product) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant submits “Shotaro teaches recommending stores based on correlation, but does not teach determining a unique sales page from multiple sales pages using an evaluation index. See Shotaro, Abstract.” [Applicant’s Remarks, 04/13/2026, page 11]
The Examiner respectfully disagrees. With respect to the §103 rejection of independent claim 1, Applicant argues that Shotaro teaches recommending stores based on correlation, but does not teach determining a unique sales page from multiple sales pages using an evaluation index.” However, in at least pages 3, 7, 13, Shotaro teaches the disputed limitation. In particular, Shotaro teaches determining a unique sales page from multiple candidate using an evaluation index including metrics such as click-through rate (CTR) and conversion rate. Shotaro’s system uses these indices to select the most relevant store or product recommendation for the user, which is equivalent to selecting a unique sales page based on an evaluation index. Thus, given the broadest reasonable interpretation consistent with the specification in construing the claimed invention, it is Examiner’s position that the disclosure of Shotaro teaches the disputed limitation. For the reasons above, this argument is found unpersuasive.
Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 112
22. 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.
23. Claim 13 is 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 pre-AIA the applicant regards as the invention.
24. Claim 13 was amended to recite “acquire a list of sales page identification information identifying each sales page for one or more products associated with each user; convert a list of sales page identification information identifying each sales page for one or more products associated with each user…” The phrases “each user” lack antecedent basis and therefore render the claim indefinite. Appropriate correction is required.
25. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
26. 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.
27. Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the apparatus (claims 1-11), method (claim 12), non-transitory computer readable medium (claim 13), and are directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, and respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-13 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), and managing commercial interactions (e.g., advertising, marketing or sales activities or behaviors; business relations), the claims also recite an abstract idea that falls into the “Mental Processes” or concepts performed in the human mind such as via observation, evaluation, and judgment. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: at least one memory storing instructions; and at least one processor configured to execute the instructions stored in the at least one memory, the instructions comprising: acquiring code configured to cause at least one of the at least one processor to acquire a list of sales page identification information identifying each sales page for one or more products associated with a user; converting code configured to cause at least one of the at least one processor to convert the list of sales page identification information into a list of product identification information identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information; generating code configured to cause at least one of the at least one processor to convert product identification information included in the list of product identification information into a user vector representation using a machine learning model, and generate a recommended product list including recommended product identification information identifying each of one or more products to be recommended to the user by determining, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist; and determining code configured to cause at least one of the at least one processor to specify one or more sales pages selling each of the one or more products to be recommended to the user based on the one or more pieces of recommended product identification information included in the recommended product list, and determine a unique sales page for each of the one or more products to be recommended to the user, wherein when a plurality of sales pages are specified for a product to be recommended, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages. Considered together, these steps set forth an abstract idea of managing commercial interactions via rules or instructions that simply select products to recommend and corresponding sales page, which are business practices traditionally performed by humans, and which fall under the realm of managing commercial interactions (e.g., marketing or sales activities or behaviors; business relations), thus falling under the “Certain methods of organizing human activity” grouping set forth in MPEP 2106, and also recites limitations that can be accomplished mentally with the aid of pen and paper (e.g., observation, evaluation, judgement, or opinion) and thus fit within the “Mental Processes” abstract idea grouping. Deciding which products to recommend and selecting appropriate sales pages constitute mental processes because a human could perform each step with the aid of pen and paper.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claims 12 and 13 recite similar limitations as those discussed above and are therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: at least one memory storing instructions, at least one processor configured to execute the instructions stored in the at least one memory, acquiring code configured to, converting code configured to, generating code configured to, a machine learning model, determining code configured to (claim 1), an information processing apparatus including a memory storing instructions and at least one processor configured to execute the instructions stored in the memory, a machine learning model (claim 12), a non-transitory computer readable medium storing an information processing program, a computer, and a machine learning model (claim 13). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “acquire” step is evaluated as an additional element, this step amount at most to insignificant extra-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: at least one memory storing instructions, at least one processor configured to execute the instructions stored in the at least one memory, acquiring code configured to, converting code configured to, generating code configured to, a machine learning model, determining code configured to (claim 1), an information processing apparatus including a memory storing instructions and at least one processor configured to execute the instructions stored in the memory, a machine learning model (claim 12), a non-transitory computer readable medium storing an information processing program, a computer, and a machine learning model (claim 13). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraphs [0036, 0052]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the “acquiring” step, this step amounts to insignificant extra-solution activity, which does not amount to a practical application (MPEP 2106.05(g)), nor add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Even if the machine learning model was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Anders et al., US 2020/0020015 (paragraph 101: “inferences may be performed by any combination of means known in the art, such as by pattern-matching, text analytics, semantic analytics, statistical methods, artificial intelligence, Bayesian analysis, machine learning, or keyword searching”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-11 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-11 recite “wherein the determining code determine a unique sales page for each of one or more products identified by the one or more pieces of recommended product identification information,” “specify one or more sales pages selling the one or more products identified by the one or more pieces of recommended product identification information, and determine the unique sales page based on the one or more specified sales pages,” “wherein if the determining causes to specify one sales page selling a product identified by the recommended product identification information, the determining causes to determine the specified sales page as the unique sales page, and if the determining cause to specify a plurality of sales pages selling a product identified by the recommended product identification information, the determining causes to determine the unique sales page out of the plurality of specified sales,” “wherein the evaluation index includes a conversion rate (CVR) for each of the plurality of specified sales pages,” “wherein the evaluation index includes an evaluation score given by a user to each of the plurality of specified sales pages,” “convert, for the user, product identification information included in the list of product identification information into a user vector representation using a model; and determine, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist,” “select one or more genres in which the user shows an interest, acquire the list of sales page identification information for the one or more genres selected for the user,” “select one or more genres for the user using a model,” “select one or more genres for the user using a model,” “ determine a sales page for each of one or more products to be recommended to another user based on the list of product identification information,” however these limitations are part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings. The dependent claims recite additional elements of: vector converting code configured to and using a machine learning model (claim 7), selecting code configured to (claim 8), the selecting code and using a matrix factorization (MF)-based machine learning model (claim 9), the selecting code and using a natural language processing-based machine learning model (claim 10). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
Even if the machine learning model was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality, this element amounts to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 0039: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”).
Even if the a matrix factorization (MF)-based machine learning model was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality, this element amounts to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Park et al., US 2023/0186105 (paragraph 0013: “Matrix factorization is one approach, which is well-known in the field of the recommender system.”).
Even if the natural language processing-based machine learning model was evaluated as an element beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality, this element amounts to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Grois., US 2008/0256064 (paragraph 0124: “conventional natural language processing techniques can be implemented for enabling a semantic analysis of said recommendations”).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
28. 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.
29. 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 of this title, 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.
30. The factual inquiries 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.
31. Claims 1-3, 7-8, and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Stoppelman, Pub. No.: US 2007/0005437 A1, [hereinafter Stoppelman], in view of Shotaro et al., Pub. No.: JP 2018-147045 A, [hereinafter Shotaro] (machine translation used for citations: Document U: JP_2018147045_A_I_Machine Translation, pages 1-27), in view of Liu et al., Pub. No.: US 2022/0230203 A1, [hereinafter Liu].
As per claim 1, Stoppelman teaches an information processing apparatus (paragraph 0009) comprising:
at least one memory storing instructions (paragraph 0033, discussing that the client/server entity may perform these operations in response to the processor executing software instructions contained in a computer-readable medium, such as a memory...; paragraph 0034); and
at least one processor configured to execute the instructions stored in the at least one memory (paragraph 0031, discussing that the processor may include a processor, microprocessor, or processing logic that may interpret and execute instructions. Main memory may include a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor…; paragraph 0033, discussing that the client/server entity may perform these operations in response to the processor executing software instructions contained in a computer-readable medium, such as a memory...; paragraph 0034),
the instructions (paragraph 0033) comprising: acquiring code configured to cause at least one of the at least one processor to acquire a list of sales page identification information identifying each sales page for one or more products associated with a user (paragraph 0009, discussing a system including a data collector to obtain information relating to user purchases from a group of web retailers; paragraph 0022, discussing that user behavior data, such as information associated with user purchases (conversions) and information associated with product pages and/or product information pages users accessed and how much time the users spent accessing the product pages and/or product information pages, may be collected with regard to many users and many web retailers...This information may then be used to provide product recommendations to a user; paragraph 0038, discussing that the user behavior data may also include information relating to what product pages users accessed and how long the users spent accessing these pages. In one implementation, clients may provide information relating to product pages users accessed and how long the users spent accessing these pages to data collector. For example, a client may contain software that monitors a user's web activities to assist in making the user's online experience more useful. The toolbar software may periodically provide information (e.g., Uniform Resource Locators (URLs)) relating to product pages the user accessed and how long the user spent accessing these pages to data collector. In another implementation, the data collector may obtain information relating to product pages users accessed and how long the users spent accessing these pages in another way, such as from web retailer servers. From this information, the data collector may identify products that users accessed during the same online session and/or products that the users spent a lot of time accessing (which may infer an interest in those products) during the session…; paragraph 0036);
converting code configured to cause at least one of the at least one processor to convert the list of sales page identification information into a list of product identification information identifying a product (paragraph 0036, discussing that data collector identifies products that users purchased together. For example, a web retailer server might inform the data collector that a user purchased Crest toothpaste and a Reach toothbrush during the same online session (e.g., in the same purchase transaction)…; paragraph 0037, discussing that it may be possible for different web retailer servers to label the same product differently. In this case, the data collector may normalize information relating to product labels or names. For example, if web retailer server A called Crest MultiCare Cool Mint toothpaste "Crest mc cm tp," web retailer server B called it "Crest mc cm toothpaste," and web retailer server C called it "Crest MultiCare Cool Mint toothpaste," the data collector may normalize this information to "Crest MultiCare Cool Mint toothpaste," or some other consistent variation. Alternatively, recommended products identifier may normalize the information when analyzing the user behavior data to identify product recommendations; paragraph 0048, discussing that a database that maps products to their product recommendations may be created based on the user behavior data. To create the database, the user behavior data may be analyzed and normalized to identify recommended product information associated with each product name…);
generating code configured to cause at least one of the at least one processor to convert product identification information included in the list of product identification information (paragraph 0036, discussing that data collector identifies products that users purchased together. For example, a web retailer server might inform the data collector that a user purchased Crest toothpaste and a Reach toothbrush during the same online session (e.g., in the same purchase transaction)…; paragraph 0037, discussing that it may be possible for different web retailer servers to label the same product differently. In this case, the data collector may normalize information relating to product labels or names. For example, if web retailer server A called Crest MultiCare Cool Mint toothpaste "Crest mc cm tp," web retailer server B called it "Crest mc cm toothpaste," and web retailer server C called it "Crest MultiCare Cool Mint toothpaste," the data collector may normalize this information to "Crest MultiCare Cool Mint toothpaste," or some other consistent variation. Alternatively, recommended products identifier may normalize the information when analyzing the user behavior data to identify product recommendations; paragraph 0048, discussing that a database that maps products to their product recommendations may be created based on the user behavior data. To create the database, the user behavior data may be analyzed and normalized to identify recommended product information associated with each product name…), and
generate a recommended product list including recommended product identification information identifying each of one or more products to be recommended to the user (paragraph 0027, discussing that the server may include a product recommendation system to provide product recommendations to users…; paragraph 0045, discussing that the recommended products supplier 440 may supply product recommendations from the database to the clients on behalf of web retailer servers. For example, the recommended products supplier may provide product recommendations for display within product pages associated with web sites of web retailer servers. In an alternative implementation, the recommended products supplier may provide product recommendations to web retailer servers for inclusion on their product pages); and
determining code configured to cause at least one of the at least one processor to provide one or more sales pages selling each of the one or more products to be recommended to the user based on the one or more pieces of recommended product identification information included in the recommended product list (paragraph 0050, discussing that the product recommendation system may transmit product recommendations to the client. The product recommendations may be provided in conjunction with the product page from the web retailers. For example, the product recommendations may be integrated and displayed with the product page or provided for display within a pop-up window, or the like, in conjunction with the product page).
While Stoppelman describes providing one or more sales pages, Stoppelman does not explicitly teach identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information; convert product identification information included in the list of product identification information into a user vector representation using a machine learning model; generate a recommended product list including recommended product identification information identifying each of one or more products to be recommended to the user by determining, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist; and specify one or more sales pages selling each of the one or more products to be recommended to the user based on the one or more pieces of recommended product identification information included in the recommended product list, and determine a unique sales page for each of the one or more products to be recommended to the user, wherein when a plurality of sales pages are specified for a product to be recommended, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages. Shotaro in the analogous art of recommendation systems teaches:
convert product identification information included in the list of product identification information into a user vector representation (page 5, paragraph 1, discussing that the search device generates a word vector such as (product X01, product X02, product X08, product X25,...) As a word vector corresponding to the user U01. This word vector means that queries such as “product X01”, “product X02”, “product X08”, and “product X25” are included in the query information Q01. In the example of FIG. 1, an example in which a specific product name such as “product X01” is described in the word vector is shown, but the search device is not specific information such as a product name. An identifier for identifying a product may be used…When each store manages a product using an identifier, the search device acquires the identifier as a query and generates a word vector of the user U01; page 6, paragraph 8, discussing that the search device generates a word vector corresponding to the user U01; page 15, paragraph 5, discussing that the search unit searches for a store to be recommended to the user based on the correlation between the user vector based on the query and the store vector based on a plurality of products provided by the store. That is, the search unit uses the correlation between the vector generated based on the name of the product browsed or purchased by the user and the vector generated based on the name of the product handled by the store. Search for stores to recommend to. Alternatively, the search unit 135 uses the correlation between the vector generated based on the identifier of the product viewed or purchased by the user and the vector generated based on the identifier of the product), and
generate a recommended product list including recommended product identification information identifying each of one or more products to be recommended to the user by determining, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist (page 6, paragraph 8 & page 7, paragraph 1, discussing that the search device generates a word vector corresponding to the user U01 and a word vector corresponding to each store. And the search device calculates the correlation of the word vector of the user U01 and the word vector corresponding to each store, for example. Specifically, the search device calculates the cosine similarity between the word vector of the user U01 and the word vector corresponding to each store. Then, the search device determines that the store having the highest cosine similarity is store recommended to the user U01; page 14, paragraph 6, discussing that the search unit can perform a search process that takes into account matching of store and user attribute information, user reaction information, and the like in addition to the similarity between vectors; page 18, paragraph 1, discussing that the search device calculates the cosine similarity between the user vector and the store vector); and
specify one or more sales pages selling each of the one or more products to be recommended to the user based on the one or more pieces of recommended product identification information included in the recommended product list (abstract, discussing an acquisition unit acquires a behavior history on a network of a user. The extraction unit extracts a query corresponding to the user from the behavior history acquired by the acquisition unit. The retrieval unit retrieves a store recommended to the user, on the basis of correlation between the query extracted by the extraction unit and a plurality of articles which the store provides; page 2, paragraph 3, discussing that the search device is a server device that holds information regarding recommendations. The search device is characterized in that not only a single product is searched as a recommendation, but a store itself that handles a plurality of products is handled as a recommendation. That is, the search device distributes a store [i.e., sales page] including a plurality of product information to the user as a recommendation; page 2, paragraph 4, discussing that the search device distributes a store including a plurality of product recommendations as a recommendation to the user; page 3, paragraph 5, discussing that as a recommendation displayed on the web page of the shopping site, a product that is traded in any store in the shopping site is displayed as a recommendation; page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user. Further, the search device searches for a plurality of products recommended to the user using the store as an AND condition. Thereby, the user can discover a store in line with his / her interest and receive recommendations for a plurality of products handled by the store. In other words, the search device can recommend various products appealing to the user; page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store. Alternatively, the search unit may search for a store recommended to the user based on a correlation between an identifier of a predetermined product and an identifier set for each of a plurality of products provided by the store; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...The distribution unit may distribute the store itself searched by the search unit as a recommendation. In this case, the user can access a store page that is a link destination of the recommendation, for example, by clicking a recommendation of the distributed store; page 10, paragraph 5), and
determine a unique sales page for each of the one or more products to be recommended to the user (page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user; page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store; page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...; page 19, paragraph 7 & page 20, paragraph 1, discussing that for example, when the surface on which the recommendation is displayed is a page of a store that handles groceries among the stores of the shopping service A02, the search device has a weight that preferentially recommends a store that handles groceries…As a result, the search device can preferentially distribute a store highly relevant to the page that the user is currently browsing as a recommendation),
wherein when a plurality of sales pages are specified for a product to be recommended, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages (page 3, paragraph 5, discussing that as a recommendation displayed on the web page of the shopping site, a product that is traded in any store in the shopping site is displayed as a recommendation; page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user. Further, the search device searches for a plurality of products recommended to the user using the store as an AND condition. Thereby, the user can discover a store in line with his / her interest and receive recommendations for a plurality of products handled by the store. In other words, the search device can recommend various products appealing to the user; page 13, paragraph 5, discussing that the acquisition unit acquires at least one of the number or rate at which a recommendation is selected from the user or the number or rate at which conversion related to the recommendation is achieved as a user response to the recommendation…The conversion related to the recommendation indicates, for example, that the user clicked on the recommended product, or that the user actually purchased the recommended product. That is, the acquisition unit may acquire an index value indicating the appeal effect such as CTR (Click Through Rate) or CVR (Conversion Rate); page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store. Alternatively, the search unit may search for a store recommended to the user based on a correlation between an identifier of a predetermined product and an identifier set for each of a plurality of products provided by the store; page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...The distribution unit may distribute the store itself searched by the search unit as a recommendation. In this case, the user can access a store page that is a link destination of the recommendation, for example, by clicking a recommendation of the distributed store; page 18, paragraph 6).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s features for including converting product identification information included in the list of product identification information into a user vector representation, generating a recommended product list including recommended product identification information identifying each of one or more products to be recommended to the user by determining, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist, specifying one or more sales pages selling each of the one or more products to be recommended to the user based on the one or more pieces of recommended product identification information included in the recommended product list, and determining a unique sales page for each of the one or more products to be recommended to the user, wherein when a plurality of sales pages are specified for a product to be recommended, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of sales pages based on an evaluation index associated with each of the plurality of sales pages, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Stoppelman-Shotaro combination does not explicitly teach identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information; and using a machine learning model. Liu in the analogous art of product conversion analysis teaches:
identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information (paragraph 0060, discussing that the model segmentation system generates the training data from multiple data tables. For example, in example implementations, the model segmentation system accesses a hit-level web browsing table that includes cookie identifier for client devices, a hit identifier, a website visited, and a timestamp. In some implementations, the hit-level web browsing table includes an order identifier and a product identifier (e.g., product SKU) when a product is purchased. In addition, the model segmentation system accesses a webpage product mapping table that maps webpages to segments and/or a product identifier mapping table that maps product identifiers to segments. In these implementations, the model segmentation system merges, combines, and transforms the data across these tables to generate the training data); and
using a machine learning model (paragraph 0037, discussing that the term “machine-learning model” refers to computer-implemented algorithms that model high-level abstractions in data by generating data-driven predictions or decisions from the known input data. Examples of machine-learning models include computer representations that are tunable (e.g., trained) based on inputs to approximate unknown functions. For instance, a machine-learning model includes a model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For example, machine-learning models include decision tree models, random forest models, gradient boosting models (e.g., XGBoost), neural networks, linear regression models, logistical regression models, or support vector machines (SVM) models; paragraph 0046, discussing that the model segmentation system trains and utilizes a multi-class decision tree machine-learning model to generate a decision tree that includes multiple audience segments; paragraph 0047).
The Stoppelman-Shotaro combination describes features related to data analysis and product recommendations. Liu is directed a method and system for analyzing product conversion. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Stoppelman-Shotaro combination with Liu because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying the Stoppelman-Shotaro combination to include Liu’s features for including identifying a product using a product conversion table that associates each product identification information with one or more pieces of sales page identification information, and using a machine learning model, in the manner claimed, would serve the motivation of improving the classification reach and accuracy of the model (Liu at paragraph 0123); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 1. Although not explicitly taught by Stoppelman, Shotaro in the analogous art of recommendation systems teaches wherein the determining code causes at least one of the at least one processor to determine a unique sales page for each of one or more products identified by the one or more pieces of recommended product identification information (page 2, paragraph 3, discussing that the search device is a server device that holds information regarding recommendations. The search device is characterized in that not only a single product is searched as a recommendation, but a store itself that handles a plurality of products is handled as a recommendation. That is, the search device distributes a store including a plurality of product information to the user as a recommendation; page 2, paragraph 4, discussing that the search device distributes a store including a plurality of product recommendations as a recommendation to the user; page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user; page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including wherein the determining code causes at least one of the at least one processor to determine a unique sales page for each of one or more products identified by the one or more pieces of recommended product identification information, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 2. Stoppelman further teaches wherein the determining code causes at least one of the at least one processor to specify one or more sales pages selling the one or more products identified by the one or more pieces of recommended product identification information (paragraph 0050, discussing that the product recommendation system may transmit product recommendations to the client. The product recommendations may be provided in conjunction with the product page from the web retailers. For example, the product recommendations may be integrated and displayed with the product page or provided for display within a pop-up window, or the like, in conjunction with the product page).
Stoppelman does not explicitly teach determine the unique sales page based on the one or more specified sales pages. However, Shotaro in the analogous art of recommendation systems teaches this concept. Shotaro teaches:
determine the unique sales page based on the one or more specified sales pages (page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user; page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store; page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...; page 19, paragraph 7 & page 20, paragraph 1, discussing that for example, when the surface on which the recommendation is displayed is a page of a store that handles groceries among the stores of the shopping service A02, the search device has a weight that preferentially recommends a store that handles groceries…As a result, the search device can preferentially distribute a store highly relevant to the page that the user is currently browsing as a recommendation).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including determining the unique sales page based on the one or more specified sales pages, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 7, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 1. Stoppelman further teaches wherein the instructions further comprise: vector converting code configured to cause at least one of the at least one processor to convert, for the user, product identification information included in the list of product identification information (paragraph 0036, discussing that data collector identifies products that users purchased together. For example, a web retailer server might inform the data collector that a user purchased Crest toothpaste and a Reach toothbrush during the same online session (e.g., in the same purchase transaction)…; paragraph 0037, discussing that it may be possible for different web retailer servers to label the same product differently. In this case, the data collector may normalize information relating to product labels or names. For example, if web retailer server A called Crest MultiCare Cool Mint toothpaste "Crest mc cm tp," web retailer server B called it "Crest mc cm toothpaste," and web retailer server C called it "Crest MultiCare Cool Mint toothpaste," the data collector may normalize this information to "Crest MultiCare Cool Mint toothpaste," or some other consistent variation. Alternatively, recommended products identifier may normalize the information when analyzing the user behavior data to identify product recommendations; paragraph 0048, discussing that a database that maps products to their product recommendations may be created based on the user behavior data. To create the database, the user behavior data may be analyzed and normalized to identify recommended product information associated with each product name…).
Stoppelman does not explicitly teach convert, for the user, product identification information included in the list of product identification information into a user vector representation using a machine learning model; and wherein the generating code is configured to cause at least one of the at least one processor to determine, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist. Shotaro in the analogous art of recommendation systems teaches:
convert, for the user, product identification information included in the list of product identification information into a user vector representation (page 5, paragraph 1, discussing that the search device generates a word vector such as (product X01, product X02, product X08, product X25,...) As a word vector corresponding to the user U01. This word vector means that queries such as “product X01”, “product X02”, “product X08”, and “product X25” are included in the query information Q01. In the example of FIG. 1, an example in which a specific product name such as “product X01” is described in the word vector is shown, but the search device is not specific information such as a product name. An identifier for identifying a product may be used…When each store manages a product using an identifier, the search device acquires the identifier as a query and generates a word vector of the user U01; page 6, paragraph 8, discussing that the search device generates a word vector corresponding to the user U01; page 15, paragraph 5, discussing that the search unit searches for a store to be recommended to the user based on the correlation between the user vector based on the query and the store vector based on a plurality of products provided by the store. That is, the search unit uses the correlation between the vector generated based on the name of the product browsed or purchased by the user and the vector generated based on the name of the product handled by the store. Search for stores to recommend to. Alternatively, the search unit 135 uses the correlation between the vector generated based on the identifier of the product viewed or purchased by the user and the vector generated based on the identifier of the product), and
wherein the generating code is configured to cause at least one of the at least one processor to determine, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist (page 6, paragraph 8 & page 7, paragraph 1, discussing that the search device generates a word vector corresponding to the user U01 and a word vector corresponding to each store. And the search device calculates the correlation of the word vector of the user U01 and the word vector corresponding to each store, for example. Specifically, the search device calculates the cosine similarity between the word vector of the user U01 and the word vector corresponding to each store. Then, the search device determines that the store having the highest cosine similarity is store recommended to the user U01; page 14, paragraph 6, discussing that the search unit can perform a search process that takes into account matching of store and user attribute information, user reaction information, and the like in addition to the similarity between vectors; page 18, paragraph 1, discussing that the search device calculates the cosine similarity between the user vector and the store vector).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s features for including convert, for the user, product identification information included in the list of product identification information into a user vector representation; and wherein the generating code is configured to cause at least one of the at least one processor to determine, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations each having a cosine similarity higher than a predetermined threshold with the user vector representation in a common vector space where a plurality of vector representations of the product identification information exist, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Stoppelman-Shotaro combination does not explicitly teach using a machine learning model. However, Liu in the analogous art of product conversion analysis systems teaches this concept. Liu teaches:
using a machine learning model (paragraph 0037, discussing that the term “machine-learning model” refers to computer-implemented algorithms that model high-level abstractions in data by generating data-driven predictions or decisions from the known input data. Examples of machine-learning models include computer representations that are tunable (e.g., trained) based on inputs to approximate unknown functions. For instance, a machine-learning model includes a model that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For example, machine-learning models include decision tree models, random forest models, gradient boosting models (e.g., XGBoost), neural networks, linear regression models, logistical regression models, or support vector machines (SVM) models; paragraph 0046, discussing that the model segmentation system trains and utilizes a multi-class decision tree machine-learning model to generate a decision tree that includes multiple audience segments; paragraph 0047).
The Stoppelman-Shotaro combination describes features related to data analysis and product recommendations. Liu is directed a method and system for analyzing product conversion. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Stoppelman-Shotaro combination with Liu because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying the Stoppelman-Shotaro combination to include Liu’s feature for including using a machine learning model, in the manner claimed, would serve the motivation of improving the classification reach and accuracy of the model (Liu at paragraph 0123); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 1. Stoppelman further teaches wherein the instructions further comprise: selecting code configured to cause at least one of the at least one processor to select one or more genres in which the user shows an interest (paragraph 0038, discussing that the user behavior data may also include information relating to what product pages users accessed and how long the users spent accessing these pages. In one implementation, clients may provide information relating to product pages users accessed and how long the users spent accessing these pages to the data collector. For example, a client may contain software that monitors a user's web activities to assist in making the user's online experience more useful. The toolbar software may periodically provide information (e.g., Uniform Resource Locators (URLs)) relating to product pages the user accessed and how long the user spent accessing these pages to the data collector. In another implementation, the data collector may obtain information relating to product pages users accessed and how long the users spent accessing these pages in another way, such as from web retailer servers. From this information, the data collector may identify products that users accessed during the same online session and/or products that the users spent a lot of time accessing (which may infer an interest in those products) during the session. The data collector may store this information in the corpus; paragraph 0042, discussing that the product name field may store information relating to different products that web retailer servers sell. Product name field may store information relating to a product in one or more forms of specificity. For example, product name field 512 stores information about a specific type of toothpaste (Crest MultiCare Cool Mint toothpaste); product name field 514 stores more general information about a type of toothpaste (Crest MultiCare toothpaste); and product name field 516 stores even more general information about a type of toothpaste (Crest toothpaste). The particular form of specificity may be implementation-specific or based on one or more factors, such as the amount of behavior data relating to the product in the corpus; paragraph 0044, discussing that similar to the products in product name field 510, recommended products field 520 may store information relating to a product in one or more forms of specificity. For example, recommended products field 520 may store information about a specific type of product (e.g., Glide Tape Original dental floss and Reach Performance toothbrush) and/or more general information about a type of product (Dial Deodorant soap and Clairol Herbal Essences shampoo). The particular form of specificity may be implementation-specific or based on one or more factors, such as the amount of behavior data relating to the product in the corpus).
Although not explicitly taught by Stoppelman, Shotaro in the analogous art of recommendation systems teaches wherein the acquiring code is configured to cause at least one of the at least one processor to acquire the list of sales page identification information for the one or more genres selected for the user (page 3, paragraph 3, discussing that when the user U01 performs a purchase action at a shopping site on the Internet that imitates a shopping mall, query information corresponding to the purchase action is generated. In the shopping site, for example, it is assumed that a plurality of exhibitors open their stores (stores). When the user U01 purchases drinking water at a predetermined store in the shopping site, the query information of the user U01 includes the name of the purchased drinking water (that is, a product) and a product category such as “drinking water”; page 11, paragraph 7, discussing that the store attribute information stores specific information such as what attributes the store has. For example, the store attribute information includes the size of the store, the category of the product handled by the store, the attributes of the user who is the main target of the store for sale, and the like. For example, the scale of the store is set from the service side (for example, the shopping site side where the store opens) according to the capital, the number of products handled, and the like. The category of products handled by the store includes information on specific categories of products handled by the store, such as groceries and home appliances. In addition, the attributes of users that are mainly targeted by the store for sale indicate the main customer segments assumed by the store, for example, for men, women, young people, and elderly people. It should be noted that the attributes of the users that are mainly targeted by the store may be set automatically based on the product category or the like, or may be set by the shopping site or the store itself; page 19, paragraph 7 & page 20, paragraph 1, discussing that when the surface on which the recommendation is displayed is a page of a store that handles groceries among the stores of the shopping service A02, the search device has a weight that preferentially recommends a store that handles groceries. The search process may be performed with As a result, the search device can preferentially distribute a store highly relevant to the page that the user is currently browsing as a recommendation).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including wherein the acquiring code is configured to cause at least one of the at least one processor to acquire the list of sales page identification information for the one or more genres selected for the user, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 1. Stoppelman further teaches the determining code further causes at least one of the at least one processor to determine a sales page for each of one or more products to be recommended to another user based on the list of product identification information (paragraph 0021, discussing providing product recommendations based on collaborative filtering of user behavior data. For example, implementations described may leverage user behavior data associated with a group of web retailers and/or non-web retailers to provide product recommendations to users of a particular web retailer; paragraph 0050, discussing that the product recommendation system may transmit product recommendations to the client. The product recommendations may be provided in conjunction with the product page from the web retailers. For example, the product recommendations may be integrated and displayed with the product page or provided for display within a pop-up window, or the like, in conjunction with the product page; paragraph 0027, discussing that the server may include a product recommendation system to provide product recommendations to users of at least some of the servers. Server 220 may gather user behavior data associated with users' activities with regard to servers 230 and/or 240 and perform collaborative filtering of the user behavior data to provide recommendation data to clients 210; paragraph 0023).
While Stoppelman describes providing a sales page, Stoppelman does not explicitly teach determine a sales page for each of one or more products to be recommended. However, Shotaro in the analogous art of recommendation systems teaches this concept. Shotaro teaches:
Determine a sales page for each of one or more products to be recommended (abstract, discussing an acquisition unit acquires a behavior history on a network of a user. The extraction unit extracts a query corresponding to the user from the behavior history acquired by the acquisition unit. The retrieval unit retrieves a store recommended to the user, on the basis of correlation between the query extracted by the extraction unit and a plurality of articles which the store provides; page 2, paragraph 3, discussing that the search device is a server device that holds information regarding recommendations. The search device is characterized in that not only a single product is searched as a recommendation, but a store itself that handles a plurality of products is handled as a recommendation. That is, the search device distributes a store [i.e., sales page] including a plurality of product information to the user as a recommendation; page 2, paragraph 4, discussing that the search device distributes a store including a plurality of product recommendations as a recommendation to the user; page 3, paragraph 5, discussing that as a recommendation displayed on the web page of the shopping site, a product that is traded in any store in the shopping site is displayed as a recommendation; page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user. Further, the search device searches for a plurality of products recommended to the user using the store as an AND condition. Thereby, the user can discover a store in line with his / her interest and receive recommendations for a plurality of products handled by the store. In other words, the search device can recommend various products appealing to the user; page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store. Alternatively, the search unit may search for a store recommended to the user based on a correlation between an identifier of a predetermined product and an identifier set for each of a plurality of products provided by the store; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...The distribution unit may distribute the store itself searched by the search unit as a recommendation. In this case, the user can access a store page that is a link destination of the recommendation, for example, by clicking a recommendation of the distributed store; page 10, paragraph 5).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including determining a sales page for each of one or more products to be recommended, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 12 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 12 the Stoppelman-Shotaro-Liu combination teaches an information processing method to be performed by an information processing apparatus including a memory storing instructions and at least one processor configured to execute the instructions stored in the memory (paragraph 0014, discussing systems and methods consistent with the principles of the invention may be implemented; paragraph 0031, discussing that the processor may include a processor, microprocessor, or processing logic that may interpret and execute instructions; paragraph 0033, discussing that the client/server entity may perform these operations in response to the processor executing software instructions contained in a computer-readable medium, such as a memory...; paragraphs 0025, 0034).
Claim 13 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 13 the Stoppelman-Shotaro-Liu combination teaches a non-transitory computer readable medium storing an information processing program for causing a computer to (paragraph 0031, discussing that the processor may include a processor, microprocessor, or processing logic that may interpret and execute instructions; paragraph 0034, discussing that the software instructions may be read into a memory from another computer-readable medium, such as a data storage device, or from another device via a communication interface. The software instructions contained in the memory may cause the processor to perform processes).
32. Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Stoppelman in view of Shotaro, in view of Liu, in further view of Zhang, Pub. No.: US 2011/0112993 A1, [hereinafter Zhang].
As per claim 4, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 3. Although not explicitly taught by Stoppelman, Shotaro in the analogous art of recommendation systems teaches wherein if the determining code causes at least one of the at least one processor to specify a plurality of sales pages selling a product identified by the recommended product identification information, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of specified sales pages based on an evaluation index associated with each of the plurality of identified sales pages (page 3, paragraph 5, discussing that as a recommendation displayed on the web page of the shopping site, a product that is traded in any store in the shopping site is displayed as a recommendation; page 7, paragraph 8, discussing that the search device searches for a store having a high correlation with the user, and distributes the searched store as a recommendation to the user. Further, the search device searches for a plurality of products recommended to the user using the store as an AND condition. Thereby, the user can discover a store in line with his / her interest and receive recommendations for a plurality of products handled by the store. In other words, the search device can recommend various products appealing to the user; page 13, paragraph 5, discussing that the acquisition unit acquires at least one of the number or rate at which a recommendation is selected from the user or the number or rate at which conversion related to the recommendation is achieved as a user response to the recommendation…The conversion related to the recommendation indicates, for example, that the user clicked on the recommended product, or that the user actually purchased the recommended product. That is, the acquisition unit may acquire an index value indicating the appeal effect such as CTR (Click Through Rate) or CVR (Conversion Rate); page 15, paragraph 4, discussing that the search unit searches for a store to be recommended to the user based on a correlation between a name set for a predetermined product and a name set for each of a plurality of products provided by the store. Alternatively, the search unit may search for a store recommended to the user based on a correlation between an identifier of a predetermined product and an identifier set for each of a plurality of products provided by the store; page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 16, paragraph 4, discussing that the distribution unit distributes, as a recommendation, a recommendation related to a store...The distribution unit may distribute the store itself searched by the search unit as a recommendation. In this case, the user can access a store page that is a link destination of the recommendation, for example, by clicking a recommendation of the distributed store; page 18, paragraph 6).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including wherein if the determining code causes at least one of the at least one processor to specify a plurality of sales pages selling a product identified by the recommended product identification information, the determining code causes at least one of the at least one processor to determine the unique sales page out of the plurality of specified sales pages based on an evaluation index associated with each of the plurality of identified sales pages., in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Stoppelman-Shotaro-Liu combination does not explicitly teach wherein if the determining code causes at least one of the at least one processor to specify one sales page selling a product identified by the recommended product identification information, the determining code causes at least one of the at least one processor to determine the specified sales page as the unique sales page. However, Zhang in the analogous art of recommendation systems teaches this concept. Zhang teaches:
wherein if the determining code causes at least one of the at least one processor to specify one sales page selling a product identified by the recommended product identification information, the determining code causes at least one of the at least one processor to determine the specified sales page as the unique sales page (paragraph 0224, discussing that the information for the products or services can be searched by the users in the form of document structure, wherein the search results for products or services are not necessarily arranged by the release or available time, but rather for products or services that are already available. A user can input a name for the product or service, then if the name is unique in the resource database, only one result will be provided, the user can click on the name of the product or service. If only one web page is linked to the name for the product or service, then the user will be led to this web page [i.e., determines the specified sales page as the unique sales page]. If more than one web pages sell the product or service, the user will be led to the web page that displays the list of web pages containing information about the product or service. If the web pages are selling the product or service, the list of the web pages can be arranged by price offered on each web pages, or the list can be arranged by location of the product or service are offered, or other characteristics related to the providers of the product or service according to the information linked with the name of the product or service in the resource database. The user can choose various links to read information on the web pages or pay or download product or service from the web pages).
The Stoppelman-Shotaro-Liu combination describes features related to data analysis and product recommendations. Zhang is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Stoppelman-Shotaro-Liu combination with Zhang because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying the Stoppelman-Shotaro-Liu combination to include Zhang’s feature for including wherein if the determining code causes at least one of the at least one processor to specify one sales page selling a product identified by the recommended product identification information, the determining code causes at least one of the at least one processor to determine the specified sales page as the unique sales page, in the manner claimed, would serve the motivation of saving the user time and effort, and providing better results (Zhang at paragraph 0210); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Stoppelman-Shotaro-Liu-Zhang combination teaches the information processing apparatus according to claim 4. Although not explicitly taught by Stoppelman, Shotaro in the analogous art of recommendation systems teaches wherein the evaluation index includes a conversion rate (CVR) for each of the plurality of specified sales pages (page 13, paragraph 5, discussing that the acquisition unit acquires at least one of the number or rate at which a recommendation is selected from the user or the number or rate at which conversion related to the recommendation is achieved as a user response to the recommendation…The conversion related to the recommendation indicates, for example, that the user clicked on the recommended product, or that the user actually purchased the recommended product. That is, the acquisition unit may acquire an index value indicating the appeal effect such as CTR (Click Through Rate) or CVR (Conversion Rate); page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 18, paragraph 8).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including wherein the evaluation index includes a conversion rate (CVR) for each of the plurality of specified sales pages, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6, the Stoppelman-Shotaro-Liu-Zhang combination teaches the information processing apparatus according to claim 4. Although not explicitly taught by Stoppelman, Shotaro in the analogous art of recommendation systems teaches wherein the evaluation index includes an evaluation score given by a user to each of the plurality of specified sales pages (page 13, paragraph 5, discussing that the acquisition unit acquires at least one of the number or rate at which a recommendation is selected from the user or the number or rate at which conversion related to the recommendation is achieved as a user response to the recommendation…The conversion related to the recommendation indicates, for example, that the user clicked on the recommended product, or that the user actually purchased the recommended product. That is, the acquisition unit may acquire an index value indicating the appeal effect such as CTR (Click Through Rate) or CVR (Conversion Rate); page 16, page 1, discussing that when the user's reaction to the recommended store is better than other stores (for example, when the click rate or the conversion rate is high), the search unit is likely to be searched for the distribution target. Such adjustment may be performed. Alternatively, when the user's reaction to the recommended store is poor compared to other stores (for example, when the click rate or the conversion rate is low), the search unit is difficult to search for the store as a distribution target; page 18, paragraph 8, discussing that when a response is accepted, the search device identifies a store related to a recommendation that has been reacted by the user . Then, the search device updates the reaction information for the specified store. Specifically, when the reaction from the user exerts an appealing effect such as a click or a conversion, the search device determines the reaction information so that the weight of the score when calculating the store becomes heavy. On the other hand, if the response from the user does not exert the appealing effect such as the withdrawal from the web page, the search device displays the response information so that the weight of the score when calculating the store is low.).
Stoppelman is directed towards a method and system for providing product recommendations. Shotaro is directed to a recommendation system. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Stoppelman with Shotaro because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying Stoppelman to include Shotaro’s feature for including wherein the evaluation index includes an evaluation score given by a user to each of the plurality of specified sales pages, in the manner claimed, would serve the motivation of accurately providing a store that matches the interests of the user (Shotaro at page 23, paragraph 1); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
33. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Stoppelman in view of Shotaro, in view of Liu, in further view of Zhang et al., Patent No.: US 10,803,509 B1, [hereinafter Zhang].
As per claim 9, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 8. Although not explicitly taught by the Stoppelman-Shotaro-Liu combination, Zhang in the analogous art of product recommendation systems teaches wherein the selecting code causes at least one of the at least one processor to select one or more genres for the user using a matrix factorization (MF)-based machine learning model (abstract, discussing methods, systems, and non-transitory computer-readable medium for generating recommendations regarding products. A method may include determining a set of content features including one or more product attributes; determining a set of latent features; receiving a query user identifier and a query product identifier; determining a feature vector associated with the query user identifier and the query product identifier based on the set of content features and the set of latent features; col. 5, lines 64-67 & col. 6, lines 1-3, discussing that the server system may comprise a machine learning module. The one or more servers may comprise the machine learning module. The machine learning module may comprise one or more neural networks; col. 10, lines 6-10, discussing that the linear model may be trained based on the training data file. In some embodiments, the linear model may be trained on a machine learning platform. The training data file may be transformed into a format that may be required by the machine learning platform; col. 9, lines 30-41, discussing that a latent feature may be generated based on a matrix such as the one depicted above in Table 2. Based on the provided inputs based on the obtained user and product information, a matrix factorization algorithm, such as SVD++, may be utilized to predict each entry. In the context of the current disclosure, the latent factor model may utilize such matrix factorization algorithms. The predicted output for each entry of the matrix, e.g., values for Table 2, may be used as latent features. Accordingly, a value of the latent feature may be determined for each user and product pair, e.g., UUID and SKU pair).
The Stoppelman-Shotaro-Liu combination describes features related to data analysis and product recommendations. Zhang is directed a method and system for generating recommendations regarding products. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Stoppelman-Shotaro-Liu combination with Zhang because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying the Stoppelman-Shotaro-Liu combination to include Zhang’s feature for including wherein the selecting code causes at least one of the at least one processor to select one or more genres for the user using a matrix factorization (MF)-based machine learning model, in the manner claimed, would serve the motivation of enhancing user experiences (Zhang at col. 5, lines 35-37); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
34. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Stoppelman in view of Shotaro, in view of Liu, in further view of Guo et al., Patent No.: US 11,151,608 B1, [hereinafter Guo].
As per claim 10, the Stoppelman-Shotaro-Liu combination teaches the information processing apparatus according to claim 8. Although not explicitly taught by the Stoppelman-Shotaro combination, Guo in the analogous art of item recommendation systems teaches wherein the selecting code causes at least one of the at least one processor to select one or more genres for the user using a natural language processing-based machine learning model (col. 8, lines 20-37, discussing that each of the one or more purchased items may have been previously assigned to a particular item concept. In one embodiment, a machine learning model may be utilized to generate an item concept relatedness score for each item concept of the plurality of item concepts with respect to the search query; col. 8, lines 66-67 & col. 9, lines 1-9, discussing that it should be understood that any suitable machine learning model may be suitable to generate the item concept relatedness score, including, but not limited to, supervised or unsupervised machine learning, natural language processing, machine perception, computer vision, affective computing, statistical learning and classification, reinforcement learning including neural networks, search algorithms and optimization algorithm and automated reasoning; col. 9, lines 61-7 & col. 10-, lines 1-36, discussing that the module 216 may recommend a bundle of items for purchase based at least in part on receiving input corresponding to a selection (e.g., but not yet purchased) of an item. For example, the selection may correspond to selecting an item to add to the shopping cart. The module 216 may then determine the assigned item concept for the selected item. The module 216 may then compute an item concept relatedness score between the selected item concept and other item concepts (e.g., from items in a list of previously purchased items, previously selected items, previously searched items, etc.). Then, based on this score, the module 216 may determine whether to recommend to bundle one or more other item concepts (e.g., an item from the item concept) with the selected item concept (e.g., selected item). In some embodiments, for example when recommending items to bundle together in a purchase, the recommendation module 216 may determine that a user is unlikely to want to purchase an item assigned to the same item concept as another recently purchased item. For example, consider a scenario in which the user selects a video cable to add to the shopping cart. In this example, the video cable may be assigned to an item concept (e.g., “Video Cables”) that is highly conceptually related to a “Televisions” item concept (e.g., based on previous co-purchases by the user and/or other users), and thus, might otherwise be recommended to bundle together for purchase. However, if the user recently purchased a television assigned to the Televisions item concept, it may be unlikely that the user would want to purchase another television assigned to the Televisions item concept shortly after the previous television purchase. Accordingly, based at least in part on determining that a television assigned to the Televisions item concept was recently purchased (e.g., within a predetermine period of time such as 3, 6 or 12 months), the recommendation module 216 may determine to not recommend items from the Televisions item concept for bundling for this purchase; col. 12, lines 1-4, discussing that the root of a tree of product category nodes are selected as item concept nodes).
The Stoppelman-Shotaro-Liu combination describes features related to data analysis and product recommendations. Guo is directed a method and system for providing item recommendations. Therefore they are deemed to be analogous as they both are directed towards data analysis and recommendation systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Stoppelman-Shotaro-Liu combination with Guo because the references are analogous art because they are both directed to solutions for data analysis and product recommendation, which falls within applicant’s field of endeavor (recommendation systems), and because modifying the Stoppelman-Shotaro-Liu combination to include Guo’s feature for including wherein the selecting code causes at least one of the at least one processor to select one or more genres for the user using a natural language processing-based machine learning model, in the manner claimed, would serve the motivation of improving relevant search results (Guo at col. 4, lines 37-38); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arora et al., Pub. No.: US 2020/0410547 A1 – describes transmitting an indication of one or more items to advertise or promote on a website to a purchasing customer.
Tavernier, Patent No.: US 10,706,450 B1 – describes an artificial intelligence system for generating intent-aware recommendations.
Morris, Pub. No.: US 2015/0356658 A1 – describes systems and methods for serving product recommendations.
Feierfeil et al., Pub. No.: US 2017/0206581 A1 – describes a product vector for product recommendation
Saleem, Hussain, et al. "Strategic data driven approach to improve conversion rates and sales performance of e-commerce websites." International Journal of Scientific & Engineering Research 10.4 (2019): 588-593 – describes the strategic data-driven approach for the improvement of business sales performance and conversion rates of e-commerce websites.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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