CTNF 18/521,114 CTNF 101317 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-07 This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: controller model, and evaluator model in claim 6, and 17. Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof. If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim 1-20 rejected under 35 U.S.C. 101 because he claimed invention is directed to an abstract idea without significantly more. MPEP 2106 (Ill) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows. Step 1 Analysis: Claim 1-11 are directed to a device (machine). Claims 12-19 are directed to method (processes). Claims 20 are directed to a computer program product (article of manufacture). Therefore, claims 1-20 fall into one of four statutory categories (i.e., process, machine, article of manufacture). As to claim 1, Step 2A Prong 1: this claim recites the following abstract ideas: determine an embedding vector associated with each item of the set of items based on the received textual description; (this limitation describes analyzing of textual information and converting it into a mathematical representation, which is a mental process implemented in the human mind) cluster the determined embedding vector associated with each item of the set of items into a set of labels; (this limitation describes grouping or classifying information, which is a mental process implemented in the human mind) determine a set of session slots associated with the received interaction information of the set of users, based on the application of the reinforcement learning model; (this limitation describes organizing user interaction into groups or session, which is a mental process implemented in the human mind) determine a set of recommended items for a user of the set of users, based on the application of the sequential model; and (this limitation describes making a recommendation based on analyzed information, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: the claim recited the following additional elements: circuitry configured to: (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) receive interaction information of a set of users for a set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).) receive a textual description of each item of the set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i).) apply a reinforcement learning model on the set of labels based on the clustering of the determined embedding vector; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) apply a sequential model on the set of session slots associated with the received interaction information of the set of users; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) render the determined set of recommended items. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to display, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 12, Step 2A Prong 1: this claim recites the following abstract ideas: determining an embedding vector associated with each item of the set of items based on the received textual description; (this limitation describes analyzing of textual information and converting it into a mathematical representation, which is a mental process implemented in the human mind) clustering the determined embedding vector associated with each item of the set of items into a set of labels; (this limitation describes grouping or classifying information, which is a mental process implemented in the human mind) determining a set of session slots associated with the received interaction information of the set of users, based on the application of the reinforcement learning model; (this limitation describes organizing user interaction into groups or session, which is a mental process implemented in the human mind) determining a set of recommended items for a user of the set of users, based on the application of the sequential model; and (this limitation describes making a recommendation based on analyzed information, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: the claim recited the following additional elements: in an electronic device: (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) receiving interaction information of a set of users for a set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) receiving a textual description of each item of the set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) applying a reinforcement learning model on the set of labels based on the clustering of the determined embedding vector; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) applying a sequential model on the set of session slots associated with the received interaction information of the set of users (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) rendering the determined set of recommended items. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to display, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 20, Step 2A Prong 1: this claim recites the following abstract ideas: determining an embedding vector associated with each item of the set of items based on the received textual description; (this limitation describes analyzing of textual information and converting it into a mathematical representation, which is a mental process implemented in the human mind) clustering the determined embedding vector associated with each item of the set of items into a set of labels; (this limitation describes grouping or classifying information, which is a mental process implemented in the human mind) determining a set of session slots associated with the received interaction information of the set of users, based on the application of the reinforcement learning model; (this limitation describes organizing user interaction into groups or session, which is a mental process implemented in the human mind) determining a set of recommended items for a user of the set of users, based on the application of the sequential model; and (this limitation describes making a recommendation based on analyzed information, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: the claim recited the following additional elements: A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by an electronic device, causes the electronic device to execute operations, the operations comprising: (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) receiving interaction information of a set of users for a set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) receiving a textual description of each item of the set of items; (this limitation describes data transmission, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) applying a reinforcement learning model on the set of labels based on the clustering of the determined embedding vector; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) applying a sequential model on the set of session slots associated with the received interaction information of the set of users (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) rendering the determined set of recommended items. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to display, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 2, and 13 Step 2A Prong 1: this claim recites the following abstract ideas: Wherein the received interaction information corresponds to a user interaction matrix between the set of users and the set of items, and (this limitation describes organizing information in a table/matrix, which is a mental process implemented in the human mind) an entry in a cell of the user interaction matrix corresponds to an interaction between a user and an item associated with the corresponding cell of the user interaction matrix. (this limitation describes Step 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible. As to claim 3, and 14 Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1 Step 2A Prong 2 and 2B: the claim recited the following additional elements: wherein the textual description of each item includes at least one of a movie plot, a genre of the item, one or more actors of the item, one or more directors of the item, a summary of the item, or a screen play of the item. (this claim limitation describes content/data used as input, mere information gathering) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 4, and 15 Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1, Step 2A Prong 2 and 2B: the claim recited the following additional elements: wherein the clustering of the determined embedding vector is based on a K-means clustering model. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 5, and 16 Step 2A Prong 1: this claim recites the following abstract ideas: determine a set of clusters associated with the set of items, based on the determined embedding vector associated with each item of the set of items; (this limitation describes categorizing item into groups, which is a mental process implemented in the human mind) determine a centroid associated with each cluster of the determined set of clusters; (this limitation describes the mathematical calculation of a representative center point which is a mathematical concept that can be performed using a pen and paper) determine a distance of each item from the determined centroid associated with each cluster of the determined set of clusters; (this limitation describes calculating the difference or distance, which is a mathematical concept that can be performed using a pen and paper) determine a cluster closest to each item based on the determined distance; and (this limitation describes comparing distance and selecting the nearest group, which is a mental process implemented in the human mind) assign a label to each item of the set of items based on the determined closest cluster, wherein the clustering of the determined embedding vector into the set of labels is further based on the assignment of the label to each item. (this limitation describes categorizing information, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible. As to claim 6, and 17 Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1, Step 2A Prong 2 and 2B: the claim recited the following additional elements: wherein the reinforcement learning model comprises: a controller model configured to determine the set of sessions slots associated with each user of the set of users based on a set of rules, and (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) As to claim 7, and 18 Step 2A Prong 1: this claim recites the following abstract ideas: a first rule corresponding to a session slot being a set of user-item interaction time stamps, (this limitation describes rule based decision logic, which is a mental process implemented in the human mind) a second rule corresponding to a domination of one item of the set of items in a session slot, (this limitation describes organizing interaction by time, which is a mental process implemented in the human mind) a third rule corresponding to a difference of dominating items between two consecutive session slots, (this limitation describes identifying a dominant item or preference, which is a mental process implemented in the human mind) a fourth rule corresponding to a minimum number of items in a session slot being equal to two, or (the limitation describes setting a threshold rule which is a mental process implemented in the human mind) a fifth rule corresponding to a flexibility of a size of a session slot. (this limitation describes adjusting grouping size, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible. As to claim 8, and 19 Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 6, Step 2A Prong 2 and 2B: the claim recited the following additional elements: apply the controller model on the set of labels; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) determine a session slot index based on the application of the controller model, wherein the determination of the set of session slots is further based on the determined session index; (this limitation describes classifying/organizing information using a generic computer) apply the evaluator model on each session slot of the determined set of session slots; and (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) determine an evaluation result based on the application of the evaluator model, wherein the controller model is configured to optimize the set of session slots based the determined evaluation result. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) Step 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible. As to claim 9, Step 2A Prong 1: this claim recites the following abstract ideas: determine a number of dominating items in each session slot of the set of session slots; (this limitation describes counting the items that satisfy a dominance criteria within each group, which is a mental process implemented in the human mind) determine whether the number of dominating items in each session slot of the set of session slots is one; and (this limitation describes comparing the total number to the number one which is a mental process implemented in the human mind) determine the evaluation result as zero or a positive number based on the determination that the number of dominating item in each session slot of the set of session slots is one. (the limitation describes assigning a score according to a rule if score is zero or positive, which is a mental process implemented in the human mind) Step 2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2. The claim is ineligible. As to claim 10, Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 9, Step 2A Prong 2 and 2B: the claim recited the following additional elements: wherein the circuitry is further configured to determine the evaluation result as a negative number based on the determination that the number of dominating items in each session slot of the set of session slots is more than one. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. As to claim 11 Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1, Step 2A Prong 2 and 2B: the claim recited the following additional elements: wherein the sequential model is at least one of a hierarchical recurrent neural network (HRNN), a recurrent neural network (RNN), a short-term attention/memory priority (STAMP) model, a neural attentive recommendation machine (NARM) model, or a transformer model. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)) The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1, 3-10, and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. (US 20220261683 A1) in view of Krishnamurthy et al. (US 20180068371 A1) and further in view of Gibson et al. (US 9742871 B1) . As to claim 1, Mu teaches An electronic device, comprising: circuitry configured to: (see Mu paragraph [0040] “A processor unit 300 is an intelligent hardware device, (e.g., a general-purpose processing component, a digital signal processor (DSP), a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, an application specific integrated circuit (ASIC)”) receive interaction information of a set of users for a set of items; (see Mu Paragraph [0022] “the recommendation apparatus 110 receives a user interaction history including interactions of a user with a set of items”) apply a reinforcement learning model on the set of labels based on the clustering of the determined embedding vector; (see Mu paragraph [0016] “the recommendation network applies an online constraint sampling reinforcement learning (RL) algorithm”) determine a set of session slots associated with the received interaction information of the set of users, based on the application of the reinforcement learning model; (See Mu paragraph [0072] “predict a next item for the user based on the user interaction history using the recommendation network subject to the selected constraint.”) apply a sequential model on the set of session slots associated with the received interaction information of the set of users; (See Mu paragraph “The process of using the recommendation apparatus 110 to perform a sequential recommendation is further described with reference to FIG. 2.”) determine a set of recommended items for a user of the set of users, based on the application of the sequential model; and (see Mu paragraph [0088] “predict a next item for the user based on the user interaction history using the recommendation network subject to the selected constraint.”) render the determined set of recommended items. (see Mu paragraph [0026] “a user interface may enable a user 100 to interact with a device”) Mu does not explicitly teaches "receive a textual description of each item of the set of items;", "determine an embedding vector associated with each item of the set of items based on the received textual description", and "cluster the determined embedding vector associated with each item of the set of items into a set of labels" However, Krishnamurthy teaches receive a textual description of each item of the set of items; (see Krishnamurthy paragraph [0016] “the at least one computing device considers items… as words and sessions as sentences formed from these words”) determine an embedding vector associated with each item of the set of items based on the received textual description; see Krishnamurthy paragraph [0016] “The word embedding model is used by the at least one computing device to determine vector representations for the items”) Gibson teaches, cluster the determined embedding vector associated with each item of the set of items into a set of labels; (see Gibson Col [1] L [50] “Respective groups of the plurality of groups are divided into respective pluralities of clusters using a machine-learning algorithm”) It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Mu’s reinforcement-learning sequential recommendation system to include Krishnamurthy’s item-vector/embedding generation and Gibson’s clustering technique, because Krishnamurthy expressly teaches that item embeddings improve recommendation relevance by capturing item relationships (see Krishnamurthy paragraph [0017]), while Gibson expressly teaches that clustering user sessions/interactions improves personalization of content recommendations (see Gibson paragraph [0004]). A person of ordinary skill in the art would have been motivated to combine these known recommendation-improvement techniques with Mu’s sequential RL recommendation system to predictably improve recommendation accuracy, personalization, and user satisfaction. As to claim 3, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1. wherein the textual description of each item includes at least one of a movie plot, a genre of the item, one or more actors of the item, one or more directors of the item, a summary of the item, or a screen play of the item. (see Mu paragraph [0024] “The recommendation apparatus 110 predicts a next item for the user based on the user interaction history … songs that are similar in styles/genres to the user's previous interaction history.”) As to claim 4, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1. wherein the clustering of the determined embedding vector is based on a K-means clustering model. (see Gibson Col [1] L [50] “Respective groups of the plurality of groups are divided into respective pluralities of clusters using a machine-learning algorithm.”) As to claim 5, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1. determine a set of clusters associated with the set of items, based on the determined embedding vector associated with each item of the set of items; (see Gibson Col [1] L [45] “sorting a plurality of sessions… into a plurality of groups” and “Respective groups… are divided into respective pluralities of clusters using a machine-learning algorithm.”) determine a centroid associated with each cluster of the determined set of clusters; (see Gibson Col [12] L [4]“the machine-learning algorithm comprises a K-means algorithm”) determine a distance of each item from the determined centroid associated with each cluster of the determined set of clusters; (examiner note: K-means inherently computes distances between vectors/items and cluster centroids during assignment iterations. The reference teaches use of “K-means algorithm” for cluster generation.) determine a cluster closest to each item based on the determined distance; and (see Gibson Col [1] L [50] “The server system identifies the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction”) assign a label to each item of the set of items based on the determined closest cluster, (see Gibson Col [12] L [59] “during a user session and identifies (526) the user session as belonging to a first cluster of the pluralities of clusters based at least in part on the user interaction.”) wherein the clustering of the determined embedding vector into the set of labels is further based on the assignment of the label to each item. (see Gibson Col [8] L [9] “dividing respective groups into respective pluralities of clusters, identifying a user session as belonging to a first cluster”) As to claim 6, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1.wherein the reinforcement learning model comprises: a controller model configured to determine the set of sessions slots associated with each user of the set of users based on a set of rules, and (see Mu paragraph [0019] “The RL algorithm includes multiple constraints which are preselected … The RL algorithm outputs the best constraint for the policy component to follow.) examiner note: The “policy component” corresponds to a controller operating according to rules/constraints. an evaluator model configured to reward or penalize the controller model based on the set of rules. (see Mu paragraph [0016] “optimize for lifetime rewards or values…”, and see Mu paragraph [0018] “tracks reward probabilities and dropout probabilities and learns based on a lifetime value”) As to claim 7, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 6, wherein the set of rules comprises at least one of: a first rule corresponding to a session slot being a set of user-item interaction time stamps, (see Mu paragraph [0074] “An episode is a session, or a collection of virtual events, measured in the time domain from the beginning when a user joins to the end when the user leaves the session. For example, the user joining to play a video game is considered the beginning of an episode.”) a second rule corresponding to a domination of one item of the set of items in a session slot, (this optional limitation is not addressed in this office action) a third rule corresponding to a difference of dominating items between two consecutive session slots, (this optional limitation is not addressed in this office action) a fourth rule corresponding to a minimum number of items in a session slot being equal to two, or (this optional limitation is not addressed in this office action) a fifth rule corresponding to a flexibility of a size of a session slot. (this optional limitation is not addressed in this office action) As to claim 8, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 6, wherein the circuitry is further configured to: apply the controller model on the set of labels; (see Mu paragraph [0027] “The constraint sampling RL algorithm takes as input a set of constraints”) determine a session slot index based on the application of the controller model, (see Mu paragraph [0023] “The recommendation apparatus 110 selects a constraint from a set of candidate constraints”) wherein the determination of the set of session slots is further based on the determined session index; (see Mu paragraph [0024] “the recommendation network subject to the selected constraint.”) apply the evaluator model on each session slot of the determined set of session slots; and (see Mu Paragraph [0027] “tracks optimistic estimates of the value associated with each constraint” determine an evaluation result based on the application of the evaluator model, (see Mu paragraph [0016] “optimize for lifetime rewards or values” wherein the controller model is configured to optimize the set of session slots based the determined evaluation result. (see Mu paragraph [0039] “update parameters of the recommendation network based on the identified lifetime value”) As to claim 9, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 8. wherein the circuitry is further configured to: determine a number of dominating items in each session slot of the set of session slots; (see Mu paragraph [0019] “factors such as diversity and novelty”) examiner note: “factors such as diversity and novelty” imply evaluating item dominance/diversity characteristics within recommendation sequences. determine whether the number of dominating items in each session slot of the set of session slots is one; and (see Mu paragraph [0027] “tracks optimistic estimates of the value associated with each constraint”) determine the evaluation result as zero or a positive number based on the determination that the number of dominating item in each session slot of the set of session slots is one. (see Mu paragraph [0016] “optimize for lifetime rewards or values”, and see Mu paragraph [0018] “tracks reward probabilities”) As to claim 10, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 9,wherein the circuitry is further configured to determine the evaluation result as a negative number based on the determination that the number of dominating items in each session slot of the set of session slots is more than one. (see Mu paragraph [0044] “learning component 310 computes dropout probabilities”) As to claim 12, this is directed to a method that corresponds to the device of claim 1, See the rejection for claim 1 above, which also applies to claim 12. As to claim 14, this is directed to a method that corresponds to the device of claim 3, See the rejection for claim 3 above, which also applies to claim 14. As to claim 15, this is directed to a method that corresponds to the device of claim 4, See the rejection for claim 4 above, which also applies to claim 15. As to claim 16, this is directed to a method that corresponds to the device of claim 5, See the rejection for claim 5 above, which also applies to claim 16. As to claim 17, this is directed to a method that corresponds to the device of claim 6, See the rejection for claim 6 above, which also applies to claim 17. As to claim 18, this is directed to a method that corresponds to the device of claim 7, See the rejection for claim 7 above, which also applies to claim 18. As to claim 19, this is directed to a method that corresponds to the device of claim 8, See the rejection for claim 8 above, which also applies to claim 19. As to claim 20, this is directed to a computer program that corresponds to the device of claim 1, See the rejection for claim 1 above, which also applies to claim 20 . 07-21-aia AIA Claim (s) 2, 11, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. (US 20220261683 A1) in view of Krishnamurthy et al. (US 20180068371 A1) and in view of Gibson et al. (US 9742871 B1) and further in view of Hidasi et al. (SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS) . As to claim 2, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1. Mu does not explicitly teach that “the received interaction information corresponds to a user interaction matrix between the set of users and the set of items” and that “an entry in a cell of the user interaction matrix corresponds to an interaction between a user and an item associated with the corresponding cell of the user interaction matrix.” However, Hidasi teaches the received interaction information corresponds to a user interaction matrix between the set of users and the set of items; (see Hidasi section [1] “decomposing the sparse user-item interactions matrix to a set of d dimensional vectors one for each item and user in the dataset. The recommendation problem is then treated as a matrix completion/reconstruction problem”) Hidasi further teaches that an entry in a cell of the user interaction matrix corresponds to an interaction between a user and an item associated with the corresponding cell of the user interaction matrix; (see Hidasi section [1] “fill the missing entries by e.g. taking the dot product of the corresponding user–item latent factors. Factor models are hard to apply in session-based recommendation due to the absence of a user profile.”) It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Mu’s reinforcement-learning sequential recommendation system to represent the received user-item interaction information as Hidasi’s user-item interaction matrix, because Hidasi teaches that user-item interaction matrices are a known representation for recommendation systems and that recommendation can be treated as a matrix completion/reconstruction problem. A person of ordinary skill in the art would have been motivated to use such a known interaction-matrix representation in Mu’s recommendation system to organize user-item interaction histories in a structured form suitable for predicting recommended items. As to claim 11, Mu in view of Krishnamurthy and Gibson teaches the electronic device according to claim 1 wherein the sequential model is at least one of a hierarchical recurrent neural network (HRNN), a recurrent neural network (RNN), a short-term attention/memory priority (STAMP) model, a neural attentive recommendation machine (NARM) model, or a transformer model. (see Hidasi section [3.1] “We used the GRU-based RNN in our models for session-based recommendations.”) As to claim 13, this is directed to a method that corresponds to the device of claim 2, See the rejection for claim 2 above, which also applies to claim 13. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen, can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121 /Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121 Application/Control Number: 18/521,114 Page 2 Art Unit: 2121 Application/Control Number: 18/521,114 Page 3 Art Unit: 2121 Application/Control Number: 18/521,114 Page 4 Art Unit: 2121 Application/Control Number: 18/521,114 Page 5 Art Unit: 2121 Application/Control Number: 18/521,114 Page 6 Art Unit: 2121 Application/Control Number: 18/521,114 Page 7 Art Unit: 2121 Application/Control Number: 18/521,114 Page 8 Art Unit: 2121 Application/Control Number: 18/521,114 Page 9 Art Unit: 2121 Application/Control Number: 18/521,114 Page 10 Art Unit: 2121 Application/Control Number: 18/521,114 Page 11 Art Unit: 2121 Application/Control Number: 18/521,114 Page 12 Art Unit: 2121 Application/Control Number: 18/521,114 Page 13 Art Unit: 2121 Application/Control Number: 18/521,114 Page 14 Art Unit: 2121 Application/Control Number: 18/521,114 Page 15 Art Unit: 2121 Application/Control Number: 18/521,114 Page 16 Art Unit: 2121 Application/Control Number: 18/521,114 Page 17 Art Unit: 2121 Application/Control Number: 18/521,114 Page 18 Art Unit: 2121 Application/Control Number: 18/521,114 Page 19 Art Unit: 2121 Application/Control Number: 18/521,114 Page 20 Art Unit: 2121 Application/Control Number: 18/521,114 Page 21 Art Unit: 2121 Application/Control Number: 18/521,114 Page 22 Art Unit: 2121 Application/Control Number: 18/521,114 Page 23 Art Unit: 2121
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