DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The disclosure is objected to because of the following informalities: In the seventh and eight lines of paragraph [0016], “but does not purchases a product” should be “but does not purchase a product”.
Appropriate correction is required.
Claim Objections
Claims 1-10 are objected to because of the following informalities: In the eighth and ninth lines of claim 1, “among a plurality of user vectors relating to the user” poses a problem because there is previous mention of “generating a plurality of user vectors relating to the user”, raising the question of whether this should be “among the plurality of user vectors relating to the user”. Given that the word “a” rather than “the” is used, Examiner applies the broadest reasonable interpretation for examining purposes, that “among a plurality of user vectors relating to the user” could, but is not required to, refer to the same plurality of user vectors relating to the user. Appropriate correction or clarification is required.
Claim 2 is objected to because of the following informalities: In the second line of claim 2, “each of pieces of the acquired information” would be better as “each of a plurality of pieces of the acquired information”. In the third line of claim 2, “a plurality of user vectors relating to the user” is given its broadest reasonable interpretation as any such plurality of user vectors. If Applicant intends for the element to mean the plurality of user vectors relating to the user recited in the fourth line of claim 1, or the plurality of user vectors relating to the user recited in the seventh and eighth lines of claim of 1, or both, the antecedent basis should be clearly specified. In the fourth line of claim 2, “the plurality of vectors” is not very clear, and poses an antecedent basis problem; it is presumed for examination purposes to refer to a plurality of vectors generated in accordance with the earlier language of claim 2. Appropriate correction and/or clarification is required.
Claim 4 is objected to because of the following informalities: There should be a period at the end of claim 4. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: In the second line of claim 6, “a plurality of user vectors relating to the user” is given its broadest reasonable interpretation as any such plurality of user vectors. If Applicant intends for the element to mean the plurality of user vectors relating to the user recited in the fourth line of claim 1, or the plurality of user vectors relating to the user recited in the seventh and eighth lines of claim 1, or both, the antecedent basis should be clearly specified. Appropriate correction or clarification is required.
Claim 8 is objected to because of the following informalities: In the second and third lines of claim 8, “the number of pieces of information relating to the plurality of previous purchase products” appears to refer back to language in claim 2, which poses an antecedent basis problem, since claim 8 does not depend from claim 2. Therefore, Applicant may wish to amend “the number of pieces of information” to “a number of pieces of information”, may wish to amend claim 8 to depend from claim 2, or may wish to inset antecedent basis parallel to that of claim 2 into claim 8. Appropriate correction is required.
Claim 11 is objected to because of the following informalities: In the tenth line of claim 11, “among a plurality of user vectors relating to the user” poses a problem because there is previous mention of “a user vector generation unit which generates a plurality of user vectors relating to the user”, raising the question of whether this should be “among the plurality of user vectors relating to the user”. Given that the word “a” rather than “the” is used, Examiner applies the broadest reasonable interpretation for examining purposes, that “among a plurality of user vectors relating to the user” could, but is not required to, refer to the same plurality of user vectors relating to the user. Appropriate correction or clarification is required.
Claim 12 is objected to because of the following informalities: A computer or information processing system, as such, does not comprise a series of steps. Therefore, claim 12 should be rephrased. The language, “comprising: by a processor, acquiring . . . generating . . . and outputting” could be rephrased as “wherein a processor of said computer is caused to perform the steps of: acquiring . . . generating . . . and outputting”. Alternatively, “therein a program causing a computer to function as an information processing system, comprising: by a processor, acquiring . . . generating . . . and outputting” could be rephrased as “therein a program, which, when implemented by a processor or processors of a computer, cause the computer to perform the steps of: acquiring . . . generating . . . and outputting”. Furthermore, in the tenth and eleventh lines of claim 12, “among a plurality of user vectors relating to the user” poses a problem because there is previous mention of “generating a plurality of user vectors relating to the user”, raising the question of whether this should be “among the plurality of user vectors relating to the user”. Given that the word “a” rather than “the” is used, Examiner applies the broadest reasonable interpretation for examining purposes, that “among a plurality of user vectors relating to the user” could, but is not required to, refer to the same plurality of user vectors relating to the user. Appropriate correction is required (or for the possible antecedent basis issue, clarification of the meaning).
It is further noted that “recommendation product” in the sixth line of claim 1, eighth line of claim 11, and the eighth line of claim 12 would sound more natural in English as either “recommended product” or “product recommendation”. Also, “previous purchase products”, used in the second line and then the fifth line of claim 1, the second and third lines of claim 2, the second line and then the third line of claim 3, the second line and then the third line of claim 4, the second line and then the third line of claim 5, the fourth and fifth lines of claim 6, the third line of claim 8, the third line and then the fifth and sixth lines of claim 11, and the fourth and then the seventh line of claim 12, would be sound more natural in English as “previously purchased products”.
Claim Interpretation
Examiner has decided against invoking 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph with respect to claim 11. It is a debatable case, but the “units” of claim 11 are not quite “means for” or “steps for”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
First, it is determined that the claims are directed to a statutory category of invention. See MPEP 2106.03 (II). In the instant case, claims 1-10 are directed to a method, and therefore fall within the statutory category of process. Claim 11 is directed to an information processing system, and therefore falls within the statutory category of machine. Claim 12 is directed to a non-transitory computer readable medium storing therein a program causing a computer to function as an information processing system, and perform recited steps, and therefore falls within the statutory category of article of manufacture. Therefore, claims 1-12 are directed to statutory subject matter under Step 1 of the Alice/Mayo test. (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. See MPEP 2106.04. The claims are analyzed to evaluate whether they recite a judicial exception (Step 2A, Prong One) as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Step 2A, Prong Two). See MPEP 2106.04. Applying Step 2A, Prong One, the claims are directed to outputting information relating to a recommendation product for a user, and thus to a judicial exception, viz., an abstract idea in the category of certain methods of organizing human activity, and specifically in commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). (Step 2A, Prong One: YES).
Proceeding to Step 2A, Prong Two, representative claim 1 recites:
An information processing method, comprising:
acquiring information relating to a plurality of previous purchase products of each user in a plurality of electronic commerce means;
generating a plurality of user vectors relating to the user on the basis of the acquired information relating to the plurality of previous purchase products; and
outputting information relating to a recommendation product for the user on the basis of the similarity between a vector generated on the basis of information relating to a target product in one electronic commerce means and at least one vector among a plurality of user vectors relating to the user.
This does not recite any limitations which meet a specific test for being significantly more than an abstract idea, and does not apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (if claim 1 even does that, as the steps do not right any specific technology, and could be performed by a human being performing mental processes, or using pen and paper, and perhaps outputting information using spoken words). The same applies to dependent claims 2 through 10, and to parallel claim 11. The same essentially applies to parallel claim 12, which does recite some technology. (Step 2A, Prong Two: NO).
Next, under Step 2B of the Alice/Mayo test, the claims are analyzed to determine whether there are additional claim limitations that individually, or as an ordered combination, ensure that the claims amount to significantly more than the abstract idea. See MPEP 2106.05. Analysis under Step 2B is largely the same as analysis under Step 2A, Prong Two, and leads to the same conclusions. There is also the additional question of whether the claims add a specific limitation other than what is well-understood, routine, and conventional activity in the field. Claims 1-10 clearly do not, as they recite nothing specifically technological. Claim 11 recites an information processing system comprising units, but from a technological perspective, this could be any generic information processing system applied to outputting information relating to a recommendation product for a user. Claim 12 recites a non-transitory computer readable medium storing therein a program causing a computer (comprising a processor) to function as an information processing system, and carry out operations parallel to the steps of claim 1. This is technological, but Avidan et al. (U.S. Patent Application Publication 2017/0193592) discloses (paragraph 25, emphasis added), “Although not illustrated, it should be appreciated that the ecommerce server 110, the merchant computer 120, and the customer computer 130 each include conventional components, such as a processor and a memory medium storing computer-readable instructions that are executable by the processor to perform various operations including those described herein. The computer-readable instructions can be stored on non-transitory computer-readable storage media of a conventional type, whether devices and/or materials.” Hence the non-transitory computer readable medium storing therein a program causing a computer to carry out operations, together with the processor, recite only well-understood, routine, and conventional technology. Claims 1-12 therefore recite nothing to raise the claimed invention to significantly more than an abstract idea, and in particular do not recite anything beyond well-understood, routine, and conventional technology. (Step 2B: NO)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, 7, 10, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Gutnik et al. (U.S. Patent Application Publication 2019/0205973) in view of Hao et al., “Diversity Regularized Interests Modeling for Recommender Systems.” As per claim 1, Gutnik discloses acquiring information relating to a plurality of previous purchase products of each user in a plurality of electronic commerce means (paragraph 33, emphasis added), “User data 330 may be gathered and maintained by the social-networking system. User data 330 may include an order history and information contained in the social graph. The order history may include past purchases made by the user either through the online social-network or through a third-party, subject to privacy settings of the user or the third-party. The order history may also include past food orders made by the user [at] one or more restaurants. As an example and not by way of limitation, the user may have ordered the nachos from Celia’s four times in the past two months.” Gutnik does not use the exact words “information processing method”, but the methods disclosed by Gutnik inherently involve information processing. See, for example, the computer system of Figure 17; see also Figures 13 and 14. Figure 13 includes box 1330, with the words, “Score each reference of the plurality of references based at least in part on the one or more order parameters and the one or more metadata items.” Figure 14 includes box 1450, with the words, “Access user data to select prompt,” and box 1460, with the words, “Send the selected prompt to the client system for display.” Yet further, the computer system of Figure 17 is explicated in paragraphs 84 through 96.
Gutnik discloses generating a user vector relating to the user on the basis of the acquired information relating to the plurality of previous purchase products (and perhaps other factors) (paragraph 38, emphasis added). “The social-networking system may determine whether users A and B are lookalike users by representing each user as a user-vector having N dimensions in an N-dimensional vector space. Each dimension in the vector space may correspond to a particular social-networking trait. After the social-networking system has generated user-vectors for two or more users, it may measure the vector similarity (e.g., Hamming distance, cosine similarity, Euclidean distance) between the two user-vectors to determine if the users may be deemed to be lookalike users.” See also paragraph 79. Gutnik does not disclose generating a plurality of user vectors relating to the user on the basis of the acquired information, but Hao teaches a plurality of user vectors for the same user (Introduction, second column on first page, emphasis added), “However, using one vector to represent the user assumes that the user only has a single preferred interest within a session. As can be seen in Figure 1, if user clicks dresses multiple times and keyboards few times during a session the learned user vector is likely very close to dresses. During the matching stage, the nearest neighbor matching algorithm matches items related to dresses. However, keyboards are also of interest to a user. Multiple vectors that represent different interests of users are thus necessary.” Hao teaches terms with mathematical symbols in section “3.1 Problem Formulation”, in the second column of the second page, and the first column of the third page. Continuing with the first column of the third page, Hao teaches (emphasis added):
A multi-interest extractor module and a diversity separator module receive the embedding of user history behaviors, and generate multiple diverse interests for each user.
In this paper, we apply the clustering process to aggregate the user’s historical behaviors into several clusters. A cluster of items represents the user’s particular interest. Here we not only design the multi-interest extraction layer to generate multiple user interest vectors, but also design diversity separators to regularize the diversity of multi-interests.
Proceeding to the sixth page, first column, Hao teaches (under the heading of “4.5 Experimental Results”, emphasis added): “It can be observed that methods employing multiple user representation vectors perform better than employing [a] single user representation vector. Therefore, multi-interest modeling is effective for modelling user’s diverse interests as well as boosting recommendation accuracy.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority to generate a plurality of user vectors relating to the user on the basis of previous purchase products, for the stated advantage of boosting recommendation accuracy when users have interests in different categories of products, making multiple user vectors requisite to represent the diversity of their interests.
Gutnik discloses outputting information relating to a recommendation product for the user on the basis of the similarity between a vector generated on the basis of information relating to a target product and at least one vector relating to the user (paragraph 5, emphasis added), “The social-networking system may perform similar analysis for other food types, other restaurants, or any other suitable entity or concept on the online social network to generate a user-preference vector for the user. The social-networking system may also access metadata associated with catalog items from third-party vendors to generate catalog-item vectors for several different catalog items. The social-networking system may then identify or generate recommendations based on the similarity between the user-preference vector and the catalog-item vectors. The social-networking system may send the recommendations to the user’s client system for display.”
Gutnik further discloses the above in paragraph 55, emphasis added: “In particular embodiments, the social-networking system may calculate the distances between the user-preference vector of the user and each of several catalog-item vectors corresponding to the different catalog items offered by the different vendors. These distances (or differences) may be calculated using any suitable method, including Hamming distance, cosine similarity, or any other suitable method. As an example and not by way of limitation, the social-networking system may calculate these vector differences using the same processes that it uses to determine lookalike users, as discussed herein. Once the social-networking system has calculated differences, it may determine which catalog-item vectors are most similar to the user-preference vector for the user. Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. The social-networking system may then generate recommendations for catalog items corresponding to the ranked references, and send the generated recommendations to a client system of the user in ranked order.” See also paragraph 79 of Gutnik, which is parallel to paragraph 55, except for reciting “food service providers” instead of “catalog vendors” or plain “vendors”. Gutnik does not disclose that the outputting information relating to a recommendation product is done on the basis of at least one vector among a plurality of vectors relating to the user, but this is obvious in light of the Hao article, as quoted from above.
As per claim 6, Gutnik discloses updating and/or individualizing a user vector or vectors (paragraph 74, emphasis added), “The user-preference vector may be regularly updated to track the changing preferences of the user. Updates to the user-preference vector may be based on updates to the social graph with regard to the user. The user-preference vector may be individualized for each user based on direct input from the user (e.g., the user specifies that she like Mexican food, does not like deli food, etc.), or based on online social-networking activity of the user.” Gutnik further discloses weighting of actions, affecting vectors, and depending in part on past orders of products to be purchased (also in paragraph 74, emphasis added), “As an example and not by way of limitation, a user may initially have an affinity coefficient of zero for Tex Mex. The threshold affinity coefficient for the vector space to change from zero to one may be an affinity coefficient of 1.25. The user may place an order via the social-networking system for food from a Tex Mex restaurant, may post a photo of the food, and may give a positive rating of the food. Using the formula α = Ax + By + . . . + Cz, where A, B, . . . C represent particular actions the has taken on the online social network with respect to a particular activity or concept, and x, y, . . . z represent the weights associated with each action, the online social network may calculate an affinity between the user and Tex Mex food to be α = 1(0.75)+1(0.5)+1(0.25) = 1.5. This may be because in this example, ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.5, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a ‘1’ in the vector space for Tex Mex food. The social-networking system may perform similar calculations for each vector space in the user-preference vector.” See also paragraph 33, quoted from above with regard to claim 1, for further disclosure of acquired information relating to the plurality of previous purchase products.
Gutnik further discloses weighting the user-preference vectors of multiple users (paragraph 75, emphasis added), “In particular embodiments, the social-networking system may make recommendations based on the user-preference vectors of at least some of the invitees to an event. The idea behind this may be to recommend food for the event that would appeal to as many people as possible. To do this, the social-networking system may perform one or more analyses on the user-vectors for the invitees. In particular embodiments and to save processing power, the social-networking system may base its recommendations on those invitees who have indicated an intent to attend the event. In particular embodiments, each invitee may receive a weight that may indicate how much of an influence his or her preferences may have on the recommendations. In particular embodiments, an invitee who has indicated an intent to attend the event may be assigned a heavier weight than an invitee who has not indicated an intent to attend the event. This may be because the invitee who has not indicated an intent to attend the event may not attend the event at all, and thus his preferences may not matter at all.”
Gutnik does not disclose that a plurality of user vectors relating to the user are generated further using weights associated with the acquired information relating to the plurality of previous purchase products, but Hao teaches such a plurality of user vectors, as set forth with regard to claim 1 above. Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for the plurality of user vectors relating to the user to be generated further using weights associated with the acquired information relating to the plurality of previous purchase products, for the stated advantage (Hao) of boosting recommendation accuracy when users have interests in different categories of products, making multiple user vectors requisite to represent the diversity of their interests.
As per claim 7, assigning weights to at least a user vector, follows from the disclosure of Gutnik, as set forth above with regard to claim 6 (paragraphs 74 and 75), and also paragraph 33 (referred to above in the rejection of claim 6, and quoted from above, in the rejection of claim 1). Assigning a weight to each of the plurality of user vectors is obvious in view of Hau’s teaching of such a plurality of user vectors, as set forth above with respect to claim 1. Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for a weight to be assigned to each of the plurality of user vectors, for the stated advantage (Hao) of boosting recommendation accuracy when users have interests in different categories of products, making multiple user vectors requisite to represent the diversity of their interests.
As per claim 10, Gutnik discloses a product purchase history of the user (paragraph 33, emphasis added), “User data 330 may be gathered and maintained by the social-networking system. User data 330 may include an order history and information contained in the social graph. The order history may include past purchases made by the user either through the online social-network or through a third-party, subject to privacy settings of the user or the third-party. The order history may also include past food orders made by the user [at] one or more restaurants. As an example and not by way of limitation, the user may have ordered the nachos from Celia’s four times in the past two months.” Gutnik further discloses lookalike users (paragraph 36, emphasis added), “Lookalike users may be users that have similar attributes (e.g., social networking traits) as the first user.”
Gutnik further discloses using information regarding a purchase history of the user, and purchase histories of other users, especially lookalike users, in generating and thus outputting a recommendation product for the user (paragraph 39, emphasis added), “The recommendation engine 340 may then access lookalike data. The lookalike data may be actions that other users who are lookalike users with respect to the first user have taken on the online social network. Specifically, the lookalike data may be actions those users have taken with respect to catalog items associated with the entity in the specified area or with other equivalent entities. As an example and not by way of limitation, the recommendation engine 340 may access lookalike data for users who have eaten at Celia’s Mexican Restaurant or other Mexican restaurants. The lookalike data that is accessed may be an order history for each lookalike user. For example, a user may have ordered the cheese and chicken enchiladas seven times in the last three weeks. Additionally, the lookalike data may reveal that several users have ordered the cheese and chicken enchiladas and have given that menu an average rating of 4.4 out of 5. Since it is likely that the first user may have similar food preferences as a lookalike user, the social-networking system may recommend the chicken enchiladas to the first user based on the lookalike data.” Thus, it is at least obvious for information relating to a recommendation product for the user to be output using information regarding a purchase product history of the user.
As per claim 11, this is closely parallel to claim 1, and therefore obvious on the same grounds set forth above with regard to claim 1.
As per claim 12, this is largely parallel to claim 1, and therefore obvious chiefly on the same grounds set forth above with regard to claim 1. Further, Gutnik discloses a processor and a memory storing instructions/programming to be executed by the processor (paragraph 124, emphasis added), “In particular embodiments, memory 1904 includes main memory for storing instructions for processor 1902 to execute or data for processor 1902 to operate on. As an example and not by way of limitation, computer system 1900 may load instructions from storage 1906 or another source (such as, for example, another computer system 1900) to memory 1904. Processor 1902 may then load the instructions from memory 1904 to an internal register or internal cache. To execute the instructions, processor 1902 may retrieve the instructions from the internal register or internal cache and decode them.”
Gutnik further discloses a non-transitory computer-readable medium (paragraph 124, emphasis added), “Herein, a computer-readable non-transitory medium or media may include one or more semiconductor-based or other integrated circuits (ICs) ((such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard drive disks (HDDs), hybrid hard drive (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disc drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate.” Hence, claim 12 is likewise obvious over Gutnik in view of Hao.
Claims 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Gutnik et al. (U.S. Patent Application Publication 2019/0205973) and Hao et al., “Diversity Regularized Interests Modeling for Recommender Systems,” as applied to claim 1 above, and further in view of Tomii et al. (U.S. Patent Application Publication 2015/0084984). As per claim 3, Gutnik does not disclose that the information relating to the plurality of previous purchase products includes text data, image data, or audio data relating to the plurality of previous purchase products. However, Tomii teaches at least image data relating to a plurality of previous purchase products (paragraph 86, emphasis added), “The store server 100c is a server located in a store selling clothes. The memory 130c stores the history of goods purchased by the user. The control unit 120c provides the buying history information of the user through the communication unit 110c in response to the request form the user. The examples of the buying history information are the date of purchase, the amount of money, and the image, the color, the size, and material information of an article of clothing.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for the information relating to the plurality of previous purchase products to include text data, image data, or audio data relating to the plurality of previous purchase products for such obvious advantages as making previously purchased products readily available by their images. This could apply especially, although by no means exclusively, to articles of clothing; a user seeing an image might well be reminded of a particular skirt or jacket in the user’s wardrobe, while the information regarding an amount of money might not enable the user to recall which of several products had been purchased with that amount of money, or a similar amount.
As per claim 5, Tomii likewise teaches information relating to a plurality of previous purchase products including time information relating to the previous purchase products (paragraph 86, emphasis added), “The store server 100c is a server located in a store selling clothes. The memory 130c stores the history of goods purchased by the user. The control unit 120c provides the buying history information of the user through the communication unit 110c in response to the request form the user. The examples of the buying history information are the date of purchase, the amount of money, and the image, the color, the size, and material information of an article of clothing.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for the information relating to the plurality of previous purchase products to include time information relating to the previous purchase products, for at least the obvious advantage of applying time information in judging the likely relevance of past purchases to a user’s current wants (e.g., an article of clothing purchased two months ago might be taken as indicative of a user’s current tastes and body measurements, whereas an article of clothing purchased ten years earlier might not fit the user, or might not suit his current tastes or his desire to dress in a manner suiting his current job).
It is further noted with regard to claim 5 that Gutnik discloses time information pertaining to previous purchase products (paragraph 33, emphasis added), “The order may also include past food orders made by the user [at] one or more restaurants. As an example and not by way of limitation, the user may have ordered the nachos from Celia’s four times in the past two months.” Gutnik then discloses (paragraph 74, emphasis added), “The user-preference vector may be regularly updated to track the changing preferences of the user. Updates to the user-preference vector may be based on updates to the social graph with regard to the user.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for the information relating to the plurality of previous purchase products to include time information relating to the previous purchase products, for the stated advantage of tracking the changing preferences of a user.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Gutnik et al. (U.S. Patent Application Publication 2019/0205973), Hao et al., “Diversity Regularized Interests Modeling for Recommender Systems,” and Tomii et al. (U.S. Patent Application Publication 2015/0084984) as applied to claim 3 above, and further in view of Choi et al. (U.S. Patent Application Publication 2018/0137550). Gutnik does not disclose that the information relating to plurality of previous purchase products include summary information and/or feature information relating to the plurality of previous purchase products, but Choi teaches feature information relating to products purchased by a user (paragraph 53, emphasis added), “The purchase history information may include the names, colors, sizes, design features, and price information of products which the user purchased, but the present disclosure is not limited thereto.” Choi also teaches (paragraph 52, emphasis added), “The device 10 may create a query 310 for purchase of a product based on the user information and the purchase history information by obtaining the user information and the purchase history information stored therein. Furthermore, the device 10 may receive a set value to be used for purchase of the product for the user, and create the query 310 for purchase of the product based on the set value. In one example embodiment, the device 10 may create the query 310 for purchase of the product by analyzing the user information, the purchase history information, and purchase-related information included in the set value received form the user.” Hence, it would have been obvious to one of ordinary skill in the art of electronic commerce on the date of inventors’ Japanese priority for the information relating to the plurality of previous purchase products to include summary information and/or feature information, for at least the obvious advantage of being able to use design features of previous purchase products to create queries for purchase of an additional product or products based on, at least in part, the design features of previously purchased products, so as to query for products likely to be of interest to the user.
Non-Obvious Subject Matter
Claim 2 is objected to for informalities, rejected under 35 U.S.C. 101, and objected to as depending from a claim rejected under 35 U.S.C. 103, but recites non-obvious subject matter.
The following is a statement of reasons for the indication of allowable subject matter: As set forth above, Gutnik et al. (U.S. Patent Application Publication 2019/0205973) discloses elements of claim 1, with the Hao et al. article teaching a further element. However, Gutnik does not disclose that a vector for each of [a plurality of] pieces of the acquired information relating to the plurality of previous purchase products is generated, and a plurality of user vectors relating to the user are generated on the basis of the plurality of vectors [presumed to be the plurality of vectors resulting from performing the generating of a vector for each of a plurality of pieces of the acquired information]. No prior art of record supplies the deficiencies of Gutnik.
Claim 8 is objected to for informalities, rejected under 35 U.S.C. 101, and objected to as depending from a claim rejected under 35 U.S.C. 103, but recites non-obvious subject matter.
The following is a statement of reasons for the indication of allowable subject matter: As set forth above, Gutnik et al. (U.S. Patent Application Publication 2019/0205973) discloses elements of claim 1, with the Hao et al. article teaching a further element; Gutnik has further disclosures relevant to claim 7, from which claim 8 depends, as further set forth above in the 35 U.S.C. 103 rejections. Weighting vectors is known, but Gutnik does not disclose that the weight of each of the plurality of vectors is determined on the basis of the number of pieces of information relating to the plurality of previous purchase products used in generating the user vector. No other prior art of record supplies the deficiency of Gutnik. Jalan et al. (U.S. Patent Application Publication 2023/0319358) discloses weights associated with vectors (paragraphs 147 and 157; see also Figure 9). However, first, Jalan ‘358 does not sufficiently teach or suggest the specifics of what is recited in claim 8; secondly, Jalan ‘358 was filed after inventors’ Japanese priority date. Jalan ‘358 claims priority to U.S. Provisional Application 63/362,218, filed March 31, 2022, but this provisional does not include anything closely corresponding to the language of paragraphs 147 and 157 of Jalan ‘358.
Claim 9 is objected to for informalities, rejected under 35 U.S.C. 101, and objected to as depending from a claim rejected under 35 U.S.C. 103, but recites non-obvious subject matter.
The following is a statement of reasons for the indication of allowable subject matter: As set forth above, Gutnik et al. (U.S. Patent Application Publication 2019/0205973) discloses elements of claim 1, with the Hao et al. article teaching a further element. However, Gutnik does not disclose that, on the basis of at least one of information relating to a purchase product associated with each of a plurality of users, a user vector, and information of the target product, prediction information regarding at least one of information relating to a purchase product associated with another user, the user vector, and the information of the target product is generated. No other prior art of record supplies the deficiencies of Gutnik.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhou (U.S. Patent 10,762,551) discloses an intelligent recommendation system. Gutnik et al. (U.S. Patent 10,810,655) is the patent issued on the application published as U.S. Patent Application Publication 2019/0205973, and used as the primary reference in making rejections under 35 U.S.C. 103. Kumar (U.S. Patent 11,315,585) discloses determining musical style using a variational autoencoder. Gutnik et al. (U.S. Patent 11,145,006) disclose generating catalog-item recommendations based on social graph data. Jalan et al. (U.S. Patent 12,206,945) disclose generation of recommendations using trust-based embeddings.
McGovern et al. (U.S. Patent Application Publication 2014/0067596) disclose methods and apparatus for recommending products and services. Cheng et al. (U.S. Patent Application Publication 2015/0081471) disclose a personal recommendation scheme. Zhou (U.S. Patent Application Publication 2019/0188770) discloses an intelligent recommendation system. Gutnik et al. (U.S. Patent Application Publication 2019/0205999) disclose generating catalog-item recommendations based on social graph data. Kumar (U.S. Patent Application Publication 2020/0372924) discloses determining musical style using a variational autoencoder. Härmä et al. U.S. Patent Application Publication 2022/0309115) disclose recommending content items to a user. Jalan et al. U.S. Patent Application Publication 2023/0319358) discloses generation of recommendations using trust-based embeddings.
A machine translation of Chinese patent document CN-111402013-A, listed on Applicant’s Information Disclosure of March 12, 2025, translation from the European Patent Office, is made of record. The U.S. Patent Office does not guarantee its accuracy or suitability for specific purposes.
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/NICHOLAS D ROSEN/ Primary Examiner, Art Unit 3689 July 2, 2026
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