DETAILED ACTION
Status of Claims
• The following is an office action in response to the communication filed 04/03/2026.
• Claims 1-8, 11, 17, and 20 have been amended.
• Claims 1-20 are currently pending and have been examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of Application No. KR1020220160793, filed on 11/25/2022 has been received.
The examiner acknowledges that the instant application is a continuation of PCT/KR2023/016155, filed 10/18/2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
First, it is determined whether the claims are directed to a statutory category of invention. See MPEP 2106.03(II). In the instant case, claims 1-10 are directed to a medium, and claims 11-19 are directed to a method, and claim 20 is directed to a manufacture. Therefore, claims 1-20 are directed to statutory subject matter under Step 1 of the Alice/Mayo test (Step 1: YES).
The claims are then analyzed to determine if the claims are directed to a judicial exception. See MPEP 2106.04. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong 1 of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong 2 of Step 2A). See MPEP 2106.04.
Taking claim 1 as representative, claim 1 recites at least the following limitations that are believed to recite an abstract idea:
obtain first metadata information related to a plurality of items of content;
obtain content viewing history information related to a user;
obtain a feature vector definition based on at least one feature in the first metadata information, the feature vector definition comprising an arrangement of feature values corresponding to the at least one feature;
generate a first feature vector for the user by matching second metadata information related to at least one item of content viewed by the user in the content viewing history information related to the user to the feature vector definition, wherein the first feature vector has a fixed dimensionality corresponding to predefined feature information;
generate a plurality of second feature vectors, each of the plurality of second feature vectors corresponding to one of the plurality of items of content by matching the first metadata information related to each of the plurality of items of content to the feature vector definition, wherein each of the plurality of second feature vectors has the fixed dimensionality;
compare the first feature vector for the user with the plurality of second feature vectors of the plurality of items of content in a feature vector space; and
generate a recommendation information comprising at least one item of content to the user, among the plurality of items of content, based on a result of the comparison between first feature vector for the user and the plurality of second feature vectors of the plurality of items of content.
The above limitations recite the concept of comparing data and vectors to determine recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Specifically, the providing of recommendation information represents marketing and sales behaviors. This is illustrated in Specification [0003], which discusses recommendations in the context of goods and services. Further, these limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Specifically, the analysis of data and vectors to determine recommendations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Claims 11 and 20 recite the same abstract ideas as claim 1 and accordingly fall within the same grouping of abstract ideas. Accordingly, under Prong One of Step 2A of the MPEP, claims 1, 11, and 20 recite an abstract idea (Step 2A, Prong One: YES).
Under Prong Two of Step 2A of the MPEP, claims 1, 11, and 20 recite additional elements, such as an apparatus comprising: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory; operating an apparatus; and a non-transitory computer-readable recording medium having recorded thereon a program for executing an operation on a computer. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. As such, these computer-related limitations are not found to be sufficient to integrate the abstract idea into a practical application. Although these additional computer-related elements are recited, claims 1, 11, and 20 merely invoke such additional elements as a tool to perform the abstract idea. Implementing an abstract idea on a generic computer is not indicative of integration into a practical application. Similar to the limitations of Alice, claims 1, 11, and 20 merely recite a commonplace business method (i.e., comparing data and vectors to determine recommendations) being applied on a general purpose computer. See MPEP 2106.05(f). Furthermore, claims 1, 11, and 20 generally link the use of the abstract idea to a particular technological environment or field of use. The courts have identified various examples of limitations as merely indicating a field of use/technological environment in which to apply the abstract idea, such as specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer (see FairWarning v. Iatric Sys.). Likewise, claims 1, 11, and 20 specifying that the abstract idea of comparing data and vectors to determine recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer. As such, under Prong Two of Step 2A of the MPEP, when considered both individually and as a whole, the limitations of claims 1, 11, and 20 are not indicative of integration into a practical application (Step 2A, Prong Two: NO).
Since claims 1, 11, and 20 recite an abstract idea and fail to integrate the abstract idea into a practical application, claims 1, 11, and 20 are “directed to” an abstract idea (Step 2A: YES).
Next, under Step 2B, the claims are analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract idea. See MPEP 2106.05. The instant claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least the following reasons.
Returning to independent claims 1, 11, and 20, these claims recite additional elements, such as an apparatus comprising: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory; operating an apparatus; and a non-transitory computer-readable recording medium having recorded thereon a program for executing an operation on a computer. As discussed above with respect to Prong Two of Step 2A, although additional computer-related elements are recited, the claims merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Moreover, the limitations of claims 1, 11, and 20 are manual processes, e.g., receiving information, sending information, etc. The courts have indicated that mere automation of manual processes is not sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)). Furthermore, as discussed above with respect to Prong Two of Step 2A, claims 1 and 11 merely recite the additional elements in order to further define the field of use of the abstract idea, therein attempting to generally link the use of the abstract idea to a particular technological environment, such as the Internet or computing networks (see Ultramercial, Inc. v. Hulu, LLC. (Fed. Cir. 2014); Bilski v. Kappos (2010); MPEP 2106.05(h)). Similar to FairWarning v. Iatric Sys., claims 1, 11, and 20 specifying that the abstract idea of comparing data and vectors to determine recommendations is executed in a computer environment merely indicates a field of use in which to apply the abstract idea because this requirement merely limits the claim to the computer field, i.e., to execution on a generic computer.
Even when considered as an ordered combination, the additional elements do not add anything that is not already present when they are considered individually. In Alice Corp., the Court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘[a]dd nothing…that is not already present when the steps are considered separately’ and simply recite intermediated settlement as performed by a generic computer.” Id. (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, viewed as a whole, claims 1, 11, and 20 simply convey the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in claims 1, 11, and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO).
Dependent claims 2-10 and 12-19, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. Dependent claims 2-10 and 12-19 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions such as advertising, marketing, or sales activities or behaviors. Further, these claims, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in the MPEP, in that they recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. Dependent claims 2-10 and 12-19 fail to identify additional elements and as such, are not indicative of integration into a practical application. As such, under Step 2A, dependent claims 2-10 and 12-19 are “directed to” an abstract idea and are not integrated into a practical application. Similar to the discussion above with respect to claims 1, 11, and 20, dependent claims 2-10 and 12-19, analyzed individually and as an ordered combination, merely further define the commonplace business method being applied on a general purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. See MPEP 2106.05(f)(2). Further, these limitations generally link the use of the abstract idea to a particular technological environment or field of use. Accordingly, under the Alice/Mayo test, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-9, 11-13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over previously cited Yoon et al. (US 20190179915 A1), hereinafter Yoon, in view of newly cited Chen et al. (US 20220374761 A1), hereinafter Chen.
In regards to claim 1, Yoon discloses an apparatus comprising: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory to: (Yoon: [0017] – “an apparatus for recommending an item may comprise a processor and a memory storing at least one instruction executed by the processor”):
obtain first metadata information related to a plurality of items of content (Yoon: [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”; [0017] – “generate a metadata latent vector and an item latent vector based on an item database”; see also Fig. 1);
obtain content viewing history information related to a user (Yoon: [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”);
generate a first feature vector for the user…second metadata information related to at least one item of content viewed by the user in the content viewing history information related to the user (Yoon: [0007] – “predicting a user latent vector from user information obtained from the specific user based on the usage history database”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”);
generate a plurality of second feature vectors, each of the plurality of second feature vectors corresponding to one of the plurality of items of content…the first metadata information related to each of the plurality of items of content (Yoon: [0017] – “generate a metadata latent vector and an item latent vector based on an item database”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated to generate the recommendation list based on the scores”; [0095] – “the item latent vector of the movie i”; [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”);
compare the first feature vector for the user with the plurality of second feature vectors of the plurality of items of content in a feature vector space (Yoon: [0094] – “the recommendation item generating part 320 may calculate a score of the user for each movie using the inner product of the user latent vector and the item latent vector”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated”; [0095] – “the item latent vector of the movie i”; the examiner notes an inner product of the latent vectors is a comparison in feature vector space); and
generate a recommendation information comprising at least one item of content to the user, among the plurality of items of content, based on a result of the comparison between first feature vector for the user and the plurality of second feature vectors of the plurality of items of content (Yoon: [0094] – “the recommendation item generating part 320 may calculate a score of the user for each movie using the inner product of the user latent vector and the item latent vector”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated”; [0054] – “the user interface 600 may…display the recommendation list through the specific display device.”; [0016] – “generating of the recommendation list may further comprise generating the recommendation list by extracting the at least one recommendation item from the item database based on an inner product of the user latent vector and the item latent vector”).
Yet Yoon does not explicitly disclose obtain a feature vector definition based on at least one feature in the first metadata information, the feature vector definition comprising an arrangement of feature values corresponding to the at least one feature; generating a first vector by matching information to the feature vector definition, wherein the first feature vector has a fixed dimensionality corresponding to predefined feature information; and generating second vectors by matching information to the feature vector definition, wherein each of the plurality of second feature vectors has the fixed dimensionality.
However, Chen teaches a similar recommendation apparatus (Chen: [0054]), including
obtain a feature vector definition based on at least one feature in the first metadata information, the feature vector definition comprising an arrangement of feature values corresponding to the at least one feature (Chen: [0055] – “the number of possible entities or features defines the dimensional size of the vector space in which the content items are given vector representations (content item vectors). That is, in some implementations, the dimension of the content item vectors is defined by the number of possible features”);
generating a first vector by matching information to the feature vector definition, wherein the first feature vector has a fixed dimensionality corresponding to predefined feature information (Chen: [0059] – “a user profile vector (ref. 112) is defined, that is encoded in the same vector space as the content item vectors. That is, the user profile vector has the same dimensionality and defines values for the same set of features as the content item vectors”); [0055] – “the number of possible entities or features defines the dimensional size of the vector space in which the content items are given vector representations (content item vectors). That is, in some implementations, the dimension of the content item vectors is defined by the number of possible features. There can be any number of possible features in various implementations. In some implementations, the dimension of a given content item vector is greater than one thousand”; see also [0032]); and
generating second vectors by matching information to the feature vector definition, wherein each of the plurality of second feature vectors has the fixed dimensionality (Chen: [0055] – “the number of possible entities or features defines the dimensional size of the vector space in which the content items are given vector representations (content item vectors). That is, in some implementations, the dimension of the content item vectors is defined by the number of possible features. There can be any number of possible features in various implementations. In some implementations, the dimension of a given content item vector is greater than one thousand”; see also [0032]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the vector definition and size of Chen in the apparatus of Yoon because Yoon already discloses features and Chen is merely demonstrating that there may be a certain number of features. Additionally, it would have been obvious to have included obtain a feature vector definition based on at least one feature in the first metadata information, the feature vector definition comprising an arrangement of feature values corresponding to the at least one feature; generating a first vector by matching information to the feature vector definition, wherein the first feature vector has a fixed dimensionality corresponding to predefined feature information; and generating second vectors by matching information to the feature vector definition, wherein each of the plurality of second feature vectors has the fixed dimensionality as taught by Chen because vector dimensionality is well-known and the use of it in a recommendation setting would have solved the sparsity problem and improved the expressive ability of the model to meaningfully represent categories in the transformed space (Chen: [0029]).
In regards to claim 2, Yoon/Chen teaches the apparatus of claim 1. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: determine at least one feature information, among one or more feature information in the first metadata information and the second metadata information, and generate the first feature vector and the plurality of second feature vectors based on the at least one feature information (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies. Also, the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0086] – “first user latent vector predicting part 311 may predict or generate a first user latent vector based on at least one of the user information obtained by the user interface 600, the usage history database 500, and the item latent vector generated by the item latent vector learning part 220”; [0017] – “[0017] – “generate a metadata latent vector and an item latent vector based on an item database”).
In regards to claim 3, Yoon/Chen teaches the apparatus of claim 2. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: generate the first feature vector and the plurality of second feature vectors, from the first metadata information and the second metadata information, feature values corresponding to the at least one feature information (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies. Also, the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0086] – “first user latent vector predicting part 311 may predict or generate a first user latent vector based on at least one of the user information obtained by the user interface 600, the usage history database 500, and the item latent vector generated by the item latent vector learning part 220”; [0017] – “[0017] – “generate a metadata latent vector and an item latent vector based on an item database”).
Yet Yoon does not explicitly disclose each having a size of K; and K feature values.
However, Chen teaches a similar recommendation apparatus (Chen: [0032]), including
each having a size of K; and K feature values (Chen: [0032] – “User interest features and news article features share the same embedding space and are transformed into a fixed-length representation vector in near-real-time”; [0055] – “the number of possible entities or features defines the dimensional size of the vector space in which the content items are given vector representations (content item vectors). That is, in some implementations, the dimension of the content item vectors is defined by the number of possible features. There can be any number of possible features in various implementations. In some implementations, the dimension of a given content item vector is greater than one thousand”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Chen with Yoon for the reasons identified above with respect to claim 1.
In regards to claim 5, Yoon/Chen teaches the apparatus of claim 3. Yoon further discloses wherein the at least one processor is further configured to execute the one or more instructions to: generate the first feature vector and the plurality of second feature vectors based on a weight assigned to at least one feature value, among the feature values corresponding to the at least one feature information in the first metadata information and the second metadata information (Yoon: [0080-0082] – “an Item2Vec model is a model for generating the item latent vector, which may be performed by the item latent vector learning part 220…the second learning part 225 may use a back-propagation algorithm. The back-propagation algorithm may mean a process of updating weights of an artificial neural network as one of the algorithms for learning or training of the artificial neural network”; [0086] – “he first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; see also [0061]; [0086]).
Yet Yoon does not explicitly disclose the K feature values.
However, Chen teaches a similar recommendation apparatus (Chen: [0032]), including
the K feature values (Chen: [0032] – “User interest features and news article features share the same embedding space and are transformed into a fixed-length representation vector in near-real-time”; [0055] – “the number of possible entities or features defines the dimensional size of the vector space in which the content items are given vector representations (content item vectors). That is, in some implementations, the dimension of the content item vectors is defined by the number of possible features. There can be any number of possible features in various implementations. In some implementations, the dimension of a given content item vector is greater than one thousand”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventions to combine Chen with Yoon for the reasons identified above with respect to claim 1.
In regards to claim 6, Yoon/Chen teaches the apparatus of claim 2. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: generate the first feature vector for the user based on a number of occurrences of each feature value of the at least one feature information, in the second metadata information related to each of at least one item of content viewed by the user (Yoon: [0086] – “the first user latent vector predicting part 311 may obtain information on items used by the user from the user information obtained by the user interface 600 and/or the usage history database 500, and the first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”).
In regards to claim 7, Yoon/Chen teaches the apparatus of claim 1. Yoon further discloses
wherein the at least one item of content viewed by the user comprises a first item identical to a second item (Yoon: [0086] – “the first user latent vector predicting part 311 may obtain information on items used by the user from the user information obtained by the user interface 600 and/or the usage history database 500, and the first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”; [0017] – “generate a metadata latent vector and an item latent vector based on an item database”; the examiner notes the item obtained from the user interface or usage database is identical to the item that is then analyzed/in the item database).
In regards to claim 8, Yoon/Chen teaches the apparatus of claim 1. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: generate the recommendation information in order of high similarity to low similarity based on the result of the comparison, wherein the similarity represents a degree of proximity between the first feature vector for the user and each of the plurality of second feature vectors in the feature vector space (Yoon: [0094] – “the recommendation item generating part 320 may calculate a score of the user for each movie using the inner product of the user latent vector and the item latent vector”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated”; [0054] – “the user interface 600 may…display the recommendation list through the specific display device.”; [0016] – “generating of the recommendation list may further comprise generating the recommendation list by extracting the at least one recommendation item from the item database based on an inner product of the user latent vector and the item latent vector”).
In regards to claim 9, Yoon/Chen teaches the apparatus of claim 1. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: define a feature vector information for each of a plurality of features in the first metadata information and the second metadata information, wherein the feature vector information comprises feature values for each of the plurality of features as elements (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies. Also, the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0086] – “first user latent vector predicting part 311 may predict or generate a first user latent vector based on at least one of the user information obtained by the user interface 600, the usage history database 500, and the item latent vector generated by the item latent vector learning part 220”; [0017] – “[0017] – “generate a metadata latent vector and an item latent vector based on an item database”).
In regards to claim 11, claim 11 is directed to a method. Claim 11 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards an apparatus. The combined apparatus of Yoon/Chen teaches the limitations of claim 1 as noted above. Yoon further discloses a method of operating an apparatus, the method comprising: (Yoon: [0043]). Claim 11 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
In regards to claims 12-13 and 15, all the limitations in method claims 12-13 and 15 are closely parallel to the limitations of apparatus claims 2-3 and 5 analyzed above and rejected on the same bases.
In regards to claim 16, Yoon/Chen teaches the method of claim 12. Yoon further discloses
wherein the generating of the first feature vector for the user comprises: generating the first feature vector for the user based on a number of occurrences of each feature value of the at least one feature information, in the first metadata information related to each of the at least one item of content viewed by the user (Yoon: [0086] – “the first user latent vector predicting part 311 may obtain information on items used by the user from the user information obtained by the user interface 600 and/or the usage history database 500, and the first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”; [0017] – “generate a metadata latent vector and an item latent vector based on an item database… predict a user latent vector from user information obtained from the specific user based on the usage history database and one of the metadata latent vector and the item latent vector”).
In regards to claims 17-18, all the limitations in method claims 17-18 are closely parallel to the limitations of apparatus claims 8-9 analyzed above and rejected on the same bases.
In regards to claim 20, claim 20 is directed to a medium. Claim 20 recites limitations that are substantially parallel in nature to those addressed above for claim 1 which is directed towards an apparatus. The combined apparatus of Yoon/Chen teaches the limitations of claim 1 as noted above. Yoon further discloses a non-transitory computer-readable recording medium having recorded thereon a program for executing an operation on a computer, the operation comprising (Yoon: [0055]). Claim 20 is therefore rejected for the reasons set forth above in claim 1 and in this paragraph.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon, in view of Chen, in view of previously cited Asikainen et al. (US 20120303663 A1), hereinafter Asikainen.
In regards to claim 4, Yoon/Chen teaches the apparatus of claim 3. Yoon further discloses the plurality of items of content (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies”).
Yet Yoon/Chen does not explicitly disclose wherein K is determined by referring to a number of feature values in which a frequency of the at least one feature information in the items is greater than or equal to a threshold value.
However, Asikainen teaches a similar metadata apparatus (Asikainen: [0017]), including
wherein K is determined by referring to a number of feature values in which a frequency of the at least one feature information in the items is greater than or equal to a threshold value (Asikainen: [0085] – “the K most frequently occurring features can be selected for the fingerprint. For instance, in the case that a respective target text record is over a set length threshold, only the K most frequently occurring features are selected for the fingerprint”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the size of K of Asikainen in the apparatus of Yoon/Chen because Yoon/Chen already discloses features and Asikainen is merely demonstrating how the number of features is chosen. Additionally, it would have been obvious to have included wherein K is determined by referring to a number of feature values in which a frequency of the at least one feature information in the items is greater than or equal to a threshold value as taught by Asikainen because K features are well-known and the use of it in a recommendation setting would have improved search speed (Asikainen: [0122]).
In regards to claim 14, all the limitations in method claim 14 are closely parallel to the limitations of apparatus claim 4 analyzed above and rejected on the same bases.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon, in view of Chen, in view of previously cited Falkenberg et al. (US 20170337166 A1), hereinafter Falkenberg.
In regards to claim 10, Yoon/Chen teaches the apparatus of claim 1. Yoon further discloses
wherein the at least one processor is further configured to execute the one or more instructions to: define a plurality of feature vectors information for a plurality of features in the first metadata information and the second metadata information (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies. Also, the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0086] – “first user latent vector predicting part 311 may predict or generate a first user latent vector based on at least one of the user information obtained by the user interface 600, the usage history database 500, and the item latent vector generated by the item latent vector learning part 220”; [0017] – “[0017] – “generate a metadata latent vector and an item latent vector based on an item database”);
generate a plurality of first feature vectors for the user by matching pieces of the second metadata information related to the at least one item of content viewed by the user to the plurality of defined feature vectors (Yoon: [0086] – “the first user latent vector predicting part 311 may obtain information on items used by the user from the user information obtained by the user interface 600 and/or the usage history database 500, and the first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”);
generate a plurality of third feature vectors for each of the plurality of items of content by matching the first metadata information related to each of the plurality of items of content to the plurality of defined feature vectors (Yoon: [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”; [0017] – “generate a metadata latent vector and an item latent vector based on an item database”); and
generate the recommendation information comprising the at least one item of content to the user based on a result of comparing the plurality of first feature vectors for the user with the plurality of third feature vectors for each of the plurality of items of content (Yoon: [0094] – “the recommendation item generating part 320 may calculate a score of the user for each movie using the inner product of the user latent vector and the item latent vector”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated”; [0054] – “the user interface 600 may…display the recommendation list through the specific display device.”; [0016] – “generating of the recommendation list may further comprise generating the recommendation list by extracting the at least one recommendation item from the item database based on an inner product of the user latent vector and the item latent vector”).
Yet Yoon does not explicitly disclose comparing a sum of vectors with a sum of vectors.
However, Falkenberg teaches a similar apparatus for comparing vectors (Falkenberg: [0036]), including
comparing a sum of vectors with a sum of vectors (Falkenberg: [0036] – “The term ‘distance’ may be understood as a result of a mathematical comparison of vectors in polar coordinates. A vector sum of reference points of elements in a view port (or on a screen) may be compared to a vector sum of a reference layout of elements of a viewport (or screen) having upper and lower bounds”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the sums of Falkenberg in the apparatus of Yoon/Chen because Yoon/Chen already teaches comparing vectors and Falkenberg is merely demonstrating how this may occur. Additionally, it would have been obvious to have included comparing a sum of vectors with a sum of vectors as taught by Falkenberg because vector sums are well-known and the use of it in a recommendation setting would have provided information about vector similarity (Falkenberg: [0036]).
In regards to claim 19, Yoon/Chen teaches the method of claim 11. Yoon further discloses
define a plurality of feature vectors information for a plurality of features in the first metadata information and the second metadata information (Yoon: [0061] – “item database 400 may store metadata information such as directors, actors, production years, production costs, tags, and average ratings of a variety of movies, and external data information such as movie introduction material, news and blog of the variety of movies. Also, the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”; [0086] – “first user latent vector predicting part 311 may predict or generate a first user latent vector based on at least one of the user information obtained by the user interface 600, the usage history database 500, and the item latent vector generated by the item latent vector learning part 220”; [0017] – “[0017] – “generate a metadata latent vector and an item latent vector based on an item database”);
generating a plurality of first feature vectors for the user by matching pieces of the first metadata information related to the at least one item of content viewed by the user to the plurality of defined feature vectors (Yoon: [0086] – “the first user latent vector predicting part 311 may obtain information on items used by the user from the user information obtained by the user interface 600 and/or the usage history database 500, and the first user latent vector may be predicted or generated by weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”; [0061] – “the usage history database 500 may store usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user”);
generating a plurality of third feature vectors for each of the plurality of items of content by matching pieces of the second metadata information related to each of the plurality of items of content to the plurality of defined feature vectors (Yoon: [0053] – “item database 400 may include metadata of items and additional information such as documents in which the items appear”; [0017] – “generate a metadata latent vector and an item latent vector based on an item database”; [0086] – “weighting the corresponding item latent vector based on the number of times which each item is used and usage time of each item”); and
generating the recommendation information comprising the at least one item of content to the user based on a result of comparing the plurality of feature first vectors for the user with the plurality of third feature vectors for each of the plurality of items of content. (Yoon: [0094] – “the recommendation item generating part 320 may calculate a score of the user for each movie using the inner product of the user latent vector and the item latent vector”; [0088] – “the recommendation item generating part 320 may calculate a score of a specific item for a specific user by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated”; [0054] – “the user interface 600 may…display the recommendation list through the specific display device.”; [0016] – “generating of the recommendation list may further comprise generating the recommendation list by extracting the at least one recommendation item from the item database based on an inner product of the user latent vector and the item latent vector”).
Yet Yoon/Chen does not explicitly teach comparing a sum of vectors with a sum of vectors.
However, Falkenberg teaches a similar apparatus for comparing vectors (Falkenberg: [0036]), including
comparing a sum of vectors with a sum of vectors (Falkenberg: [0036] – “The term ‘distance’ may be understood as a result of a mathematical comparison of vectors in polar coordinates. A vector sum of reference points of elements in a view port (or on a screen) may be compared to a vector sum of a reference layout of elements of a viewport (or screen) having upper and lower bounds”).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have included the sums of Falkenberg in the apparatus of Yoon/Chen because Yoon/Chen already teaches comparing vectors and Falkenberg is merely demonstrating how this may occur. Additionally, it would have been obvious to have included comparing a sum of vectors with a sum of vectors as taught by Falkenberg because vector sums are well-known and the use of it in a recommendation setting would have provided information about vector similarity (Falkenberg: [0036]).
Response to Arguments
Applicant’s arguments, filed 04/03/2026, have been fully considered.
35 U.S.C. § 101
Applicant argues the claims are eligible because the claims do not recite “law of nature, a natural phenomenon, or subject matter that falls within the enumerated groupings of abstract ideas as provided in the revised guidance” (Remarks pages 12-15). The examiner disagrees. The MPEP enumerates groupings of abstract ideas, thereby synthesizing the holdings of various court decisions to facilitate examination. See MPEP 2106.04. Among the enumerated groupings is the Certain Methods of Organizing Human Activity grouping, which includes activity that falls within the enumerated sub-grouping of commercial or legal interactions, including advertising, marketing or sales activities or behaviors. Also among the enumerated groupings are “Mental Processes,” which recite concepts performed in the human mind, including observations, evaluations, judgments, and opinions. With respect to the claims, the examiner notes that the additional elements are not analyzed under Step 2A, Prong 1. The amendments further recite limitations such as generating vectors and comparing the vectors to generate recommendations. These amendments represent certain methods of organizing human activity. Applicant’s specification details that the recommendation may be provided for shopping purposes (see specification [0003]). In sum, the claims are directed towards marketing and sales activities and behaviors. Furthermore, the claims recite mental processes. Specifically, the analysis of data and vectors to determine recommendations are observations, evaluations, and judgements. These limitations are similar to the mental process of collecting information, analyzing it, and displaying certain results of the collection and analysis. Furthermore, with respect to Applicant’s assertion that the claim provides a technical improvement (pages 14-15), see the response to remarks in the paragraphs below. Accordingly, these claims recite Certain Methods of Organizing Human Activity and Mental Processes.
Applicant argues the claims do not recite an abstract idea and are integrated into a practical application because the claims represent “a specific technical solution that improves the functioning of content recommendation systems by enabling accurate recommendations with minimal user data and reduced computational resources” (Remarks pages 14-17). The examiner disagrees. The MPEP provides guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, the MPEP states “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement.” The MPEP further states that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art,” and that, “conversely, if the specification explicitly sets forth an improvement but in a conclusory manner…the examiner should not determine the claim improves technology” (see MPEP 2106.04). That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
While the examiner acknowledges that improvements to the functioning of a computer or to any other technology or technical field may constitute integration into a practical application (see MPEP 2106.05(a)), the instant claims do not provide a technical improvement. Rather, the claims provide an improvement to the abstract idea of comparing data and vectors to determine recommendations. With respect to Applicant’s arguments regarding the improvement to accuracy with minimal data, the examiner notes that improving accuracy is merely an improvement to the abstract idea and not a technical improvement. Furthermore, the claims do not reflect any sort of improvement to technology and merely include high level generic additional elements.
Although the claims include computer technology such as an apparatus comprising: a memory storing one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory; operating an apparatus; and a non-transitory computer-readable recording medium having recorded thereon a program for executing an operation on a computer, such elements are merely peripherally incorporated in order to implement the abstract idea. Put another way, these additional elements are merely used to apply the abstract idea of product management using comparisons in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to technological improvements but are directed to improving the product recommendations. The claimed process, while arguably resulting in a more accurate process for recommendation, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the server and/or computer components that operate the system. Rather, the claimed process is utilizing data sets related to products and users while still employing the same server and/or computer components used in conventional systems to improve recommendations, e.g. a business method, and therefore is merely applying the abstract idea using generic computing components. As such, the claims are not eligible.
Applicant argues the claims are patent eligible because “[a]s Director Squires explained in the recent decision in Ex Parte Desjardins…’claims directed to an improvement in the functioning of a computer…are patent eligible’” (Remarks pages 14-15). The examiner disagrees. Initially, as discussed above, the claims are not directed to an improvement in the functioning of a computer. Furthermore, in Ex Parte Desjardins, the claims reflected a specific improvement that addressed the technical problem of “catastrophic forgetting” in continual learning systems, while allowing artificial intelligence systems to variously optimize system performance, use less storage capacity and reduce system complexity. By contrast the technology in the instant case is merely recited at a high level and does not provide any similar technical improvement to a technical problem specific to learning systems. Thus, there is no evidence, short of Attorney argument, that the claims provide a technical improvement. Therefore, Desjardins is not analogous.
Applicant argues the claims provide significantly more because “the claimed features provide a specific improvement over conventional content recommendations systems…and does not simply append well-understood, routine or conventional activities” (Remarks pages 17-18). The examiner disagrees. One consideration when determining whether a claim recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. Evaluation of this consideration includes evaluating whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. Cases have found that additional elements are more than "apply it" or are not "mere instructions" when the claim recites a technological solution to a technological problem. Applicant's claims provide no similar technological solution to a technological problem. Rather, the claims at issue only merely recite the abstract idea of comparing data and vectors to determine recommendations along with the requirement to perform it on a set of generic computer components. For example, Applicant's claims merely recite steps of comparing data and vectors to determine recommendations with generic computer components being recited in a generic manner. The specificity of the claims is directed toward the abstract idea of comparing data and vectors to determine recommendations and not toward any technology, and accordingly, is insufficient to provide significantly more. While additional elements are included within the claims, they are claimed in a generic manner and merely perform generic functions. Applicant’s disclosure does not articulate or suggest how these additional elements function, individually or in combination, in any manner other than using generic functionality. As such, the claims represent mere instructions to apply an abstract idea to a general purpose computer.
35 U.S.C. § 102/103
Applicant argues the claims overcome the prior art because the references do not disclose or teach the highlighted language of "obtain a feature vector definition based on at least one feature in the first metadata information, the feature vector definition comprising an arrangement of feature values corresponding to the at least one feature; generate a first feature vector for the user by matching second metadata information related to at least one item of content viewed by the user in the content viewing history information related to the user to the feature vector definition, wherein the first feature vector has a fixed dimensionality corresponding to predefined feature information; generate a plurality of second feature vectors, each of the plurality of second feature vectors corresponding to one of the plurality of items of content by matching the first metadata information related to each of the plurality of items of content to the feature vector definition, wherein each of the plurality of second feature vectors has the fixed dimensionality” (Remarks pages 19-20). The examiner disagrees. Initially, the examiner notes that the amendments have necessitated a new grounds of a rejection and a new reference has been cited to teach the underlined language above. Furthermore, Yoon discloses generate a first feature vector for the user. Yoon discloses this at least in [0007], disclosing that a user latent vector may be generated for a user based on usage history. Yoon further discloses second metadata information related to at least one item of content viewed by the user in the content viewing history information related to the user, in [0061], which discloses a usage history database storing usage history information including information such as a viewing time, the number of viewings, and a rating of an individual movie for each user. Yoon additionally discloses generate a plurality of second feature vectors, each of the plurality of second feature vectors corresponding to one of the plurality of items of content, in at least [0017] and [0088], which disclose that a metadata latent vector and an item latent vector may be generated for an item, where there may be a plurality of items for which item latent vectors are generated. Yoon additionally discloses the first metadata information related to each of the plurality of items of content, in at least [0095] and [0053], disclosing than item database may include metadata of items and additional information such as documents in which the items appear, where the items may be content. Thus, the cited art teaches these limitations in the claims.
Applicant argues the claims overcome the prior art because the references do not disclose or teach the highlighted language of “compare the first feature vector for the user with the plurality of second feature vectors of the plurality of items of content in a feature vector space” (Remarks pages 19-20). The examiner disagrees. Yoon discloses this limitation in this claim. Yoon discloses this at least in [0094], disclosing that a score may be calculated for each content item using the inner product of the user latent vector and the item latent vector. Yoon further discloses in [0088] that the score of a specific item for a specific user may be obtained by using a value obtained by inner product of the item latent vector and either the first user latent vector or the second user latent vector, and scores of a plurality of items for the specific user may be respectively calculated. This is further illustrated in the Specification paragraphs [0069-0071], which discusses comparisons in vector space being determinations of similarities of the vectors. Thus, the calculation of the inner products of the two vectors, which measures the similarity of the two vectors, is a comparison in feature vector space under broadest reasonable interpretation. Accordingly, Yoon discloses this limitation in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Previously cited NPL reference U, initially cited in the Office action dated 01/06/2026, teaches recommendations based on word vector embeddings. Proximity of vectors may be determined. Behavioral patterns of users may be analyzed using vectors.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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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/ANNA MAE MITROS/Examiner, Art Unit 3689
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