DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to an apparatus.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The limitations of:
identify whether a user behavior occurred based on the user data, (mental evaluation, a human can look at data and make a mental determination if a behavior has occurred)
based on identifying the user behavior, acquire context information related to the user behavior, (mental evaluation, after determining something has occurred, a human can decide to look at more information based on the decision)
identify first knowledge data indicating relevance among information on the user behavior, the context information, and personalized information stored in the memory, (mental evaluation, a human can identify data based on looking at the given information) and
compare the first knowledge data and second knowledge data included in the knowledge graph and update the knowledge graph, (mental evaluation, a human can look at the data and compare it mentally)
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The limitations of:
a communication interface comprising communication circuitry; (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
a memory including a knowledge graph including a plurality of knowledge data and at least one command; (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
a processor connected with the memory and configured to control the electronic apparatus, wherein the processor is configured, by executing the at least one command, to: (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
receive user data from at least one external apparatus through the communication interface, (insignificant extra-solution activity, MPEP 2106.05(g))
wherein the first knowledge data and the second knowledge data include at least one entity information of the knowledge graph and information regarding a relation between the at least one entity (tying the abstract idea to a particular field of use, MPEP 2106.05(h))
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The limitations of:
a communication interface comprising communication circuitry; (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
a memory including a knowledge graph including a plurality of knowledge data and at least one command; (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
a processor connected with the memory and configured to control the electronic apparatus, wherein the processor is configured, by executing the at least one command, to: (generic computer components to carry out the abstract idea, MPEP 2106.05(f))
receive user data from at least one external apparatus through the communication interface, (insignificant extra-solution activity, MPEP 2106.05(g), transmitting data is well-known, understood, routine, and conventional, MPEP 2106.05(d)(II)(i))
wherein the first knowledge data and the second knowledge data include at least one entity information of the knowledge graph and information regarding a relation between the at least one entity (tying the abstract idea to a particular field of use, MPEP 2106.05(h))
Note that independent claim 9 recites the same substantial subject matter as independent claim 1, only differing in an embodiment. Claim 9 being a method does not meaningfully change the above analysis and is therefore subject to the same rejection.
Dependent claim 2 recites a processor, generic computer, and identifying various data and patterns, mental evaluations.
Dependent claim 3 recites updating a knowledge data, an idea of a solution or outcome, MPEP 2106.05(f).
Dependent claim 4 recites applying a weight, mental evaluation and/or mathematical concepts.
Dependent claim 5 recites identifying new information, mental evaluation, and updating the knowledge data and applying a weight, 2106.05(f) and mental evaluation/mathematical concepts.
Dependent claim 6 recites a user interface, generic computer hardware, and receiving data and updating the knowledge graph, 2106(d)(II)(i) and 2106.05(f).
Dependent claim 7 recites semantic data form, 2106.05(f).
Dependent claim 8 recites providing a recommendation, mental evaluation.
Dependent claims 10-15 correspond to dependent claims 2-7 above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bitran et al. US 2017/0039480 in view of Cao, Yixin, et al. "Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences."
Regarding claims 1 and 9, Bitran teaches “an electronic apparatus comprising: a communication interface comprising communication circuitry” (abstract “Exemplary computing devices that can provide signal data related to a user's exercise routine can include a mobile computing device (e.g., smart phone)”);
“a memory including a knowledge graph including a plurality of knowledge data and at least one command” ([0061] “In some embodiments, semantic information analyzer 262 may utilize a semantic knowledge representation, such as a relational knowledge graph.”); and
“a processor connected with the memory and configured to control the electronic apparatus, wherein the processor is configured, by executing the at least one command, to:” ([0031] “For instance, some functions may be carried out by a processor executing instructions stored in memory”)
“receive user data from at least one external apparatus through the communication interface” ([0095] “At step 310 physiological sensor data is received from a wearable computing device. The physiological sensor data describes physiological states of a user wearing a wearable computing device”), “identify whether a user behavior occurred based on the user data” ([0096] “At step 320 an exercise event inference engine is used to identify an exercise event by comparing the location data and the physiological sensor data to exercise event criteria”),
“based on identifying the user behavior, acquire context information related to the user behavior” ([0097] “At step 330, a record of the exercise event is stored in an exercise event data store that comprises a plurality of exercise events. The record comprises contextual features associated with the exercise event. The contextual features can include the specific type of exercise engaged in during the exercise event”),
“identify first knowledge data indicating relevance among information on the user behavior, the context information, and personalized information stored in the memory” ([0098] “At step 340 an exercise pattern inference engine is used to identify an exercise pattern by using a machine learning mechanism that analyzes the plurality of exercise events to identify a plurality of events having common contextual features.” which is analogous to knowledge data as it indicates relevance),
While Bitran generally teaches knowledge graphs, Cao more specifically teaches “compare the first knowledge data and second knowledge data included in the knowledge graph” (Cao pg. 3 §2.1 ¶3 “Among the side information, knowledge graphs (e.g., DBPedia [20]) show great potentials on recommendations due to its well-defined structures and adequant resources. This type of methods mostly transfer structural knowledge of entities from KG to user-item interaction modeling based on the given mapping between entities and items” KG entities to user interactions i.e. a comparison between the two), “and update the knowledge graph” (pg. 6 §5.1 “Thus, for entities and items, the enhanced item embeddings contain the relational knowledge among items that is complementary to user-item interactions, and improves item recommendation, since the entity embedding e perserves the structural knowledge in KG. Meanwhile, the entity embedding e shall be fine tuned by the additional connectivity through users and items during backpropagation”)
“wherein the first knowledge data and the second knowledge data include at least one entity information of the knowledge graph and information regarding a relation between the at least one entity” (abstract “As an example, if a user has watched several movies directed by (relation) the same person (entity), we can infer that the director relation plays a critical role when the user makes the decision, thus help to understand the user’s preference at a finer granularity.” And pg. 3 figure 1 shows entities with relations)
It would have been obvious to one having ordinary skill in the art at the time that the invention was filed to combine the teachings of Bitran with that of Cao since “since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system”. That is, by combining the two references, one would be able to better leverage a knowledge graph when wanting to infer or learn new patterns in order to recommend content.
Note that independent claim 9 recites the same substantial subject matter as independent claim 1, only differing in embodiment. The difference in embodiment, a method, is an obvious variation of the apparatus of claim 1 and therefore the claim is subject to the same rejection.
Regarding claims 2 and 10, the Bitran and Cao references have been addressed above. Bitran further teaches “wherein the processor is configured to: identify a pattern of the user data ([0022] “The plurality of exercise events are analyzed to determine or infer an exercise pattern for the user.”), “identify data having a different pattern from the pattern of the user data as data related to the user behavior” ([0022] “For example, a first exercise pattern for the user may comprise running Monday, Wednesday, and Friday morning. A second exercise pattern for the same user can comprise weight training every Tuesday, Thursday, and Saturday morning at a particular gymnasium” first and second patterns i.e. different patterns), “and identify whether the user behavior occurred based on the data related to the user behavior” ([0022] “Similarly, weather can comprise contextual information that can define exceptions to an inferred patter”)
Regarding claims 3 and 11, the Bitran and Cao references have been addressed above. Cao further teaches “wherein the processor is configured to: based on identifying that a pattern of the first knowledge data corresponds to a pattern of the second knowledge data, update the second knowledge data using information not included in the second knowledge data among the information included in the first knowledge data” (Cao pg. 6 §5.1 “Thus, for entities and items, the enhanced item embeddings contain the relational knowledge among items that is complementary to user-item interactions, and improves item recommendation, since the entity embedding e perserves the structural knowledge in KG. Meanwhile, the entity embedding e shall be fine tuned by the additional connectivity through users and items during backpropagation” additionally connectively with items not included)
Regarding claims 4 and 12, the Bitran and Cao references have been addressed above. Bitran further teaches “wherein the processor is configured to: based on identifying that a pattern of the first knowledge data and pattern of the second knowledge data not corresponding, apply a weight to the first knowledge data” ([0067] “in some embodiments, a corresponding confidence weight or confidence score may be determined regarding a determined user exercise pattern. The confidence score may be based on the strength of the pattern”)
Regarding claims 5 and 13, the Bitran and Cao references have been addressed above. Both further teach “wherein the processor is configured to: identify new entity information for an attribute not included in the second knowledge data” ([0022] “For example, a first exercise pattern for the user may comprise running Monday, Wednesday, and Friday morning. A second exercise pattern for the same user can comprise weight training every Tuesday, Thursday, and Saturday morning at a particular gymnasium” first and second patterns i.e. different patterns and is analogous to a new entity), “update the first knowledge data using the new entity information” (Cao §2.3 “Items usually correspond to entities in many fields, such as books, movies and musics, making it possible to transfer knowledge between fields. These information involving in two tasks are complementary, revealing the connectivity among items or between users and items. In terms of models, the two tasks both aim to rank candidates given a query (i.e., an entity or a user) as well as their implict or explict relatedness. For example, KG completion aims to find correct movies (e.g., Death Becomes Her) for the person Robert Zemeckis given the explicit relation isDirectorOf, while item recommendation aims at recommending movies for a target user satisfying some implicit preference” analogous to updating the graph for any given data such as the data of Bitran or Cao),” and apply a weight to the updated first knowledge data based on a number of times knowledge data of the same pattern as the updated first knowledge data is acquired” ([0067] “in some embodiments, a corresponding confidence weight or confidence score may be determined regarding a determined user exercise pattern. The confidence score may be based on the strength of the pattern”)
Regarding claims 6 and 14, the Bitran and Cao references have been addressed above. Bitran further teaches “wherein the electronic apparatus further comprises: a user interface comprising interface circuitry, and the processor is configured to:” ([0031] “Other arrangements and elements (e.g., machines, interfaces, functions, orders, and groupings of functions, etc.) can be used in addition to or instead of those shown,”) “receive input of a threshold value through the user interface, and based on the weight being greater than or equal to the threshold value, add the updated first knowledge data to the knowledge graph” ([0075] “Having determined that a pattern exists, or that the confidence score for a pattern is sufficiently high (e.g., satisfies a threshold value), exercise pattern determiner 266 may identify that a plurality of exercise events for the user corresponds to a user exercise pattern for the user.” Which would update the graph as shown above as something new is being discovered)
Regarding claims 7 and 15, the Bitran and Cao references have been addressed above. Bitran further teaches “wherein the knowledge graph includes the plurality of knowledge data in a semantic form” ([0061] “In some embodiments, semantic information analyzer 262 may utilize a semantic knowledge representation, such as a relational knowledge graph.”)
Regarding claim 8, the Bitran and Cao references have been addressed above. Bitran further teaches “wherein the processor is configured to: provide recommendation information based on the knowledge graph and the context information” ([0089] “where a user has a pattern of playing tennis, and it is determined that the user's usual opponent is unavailable, the user may be notified and recommended to reschedule her tennis game”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wenige, Lisa, and Johannes Ruhland. "Similarity-based knowledge graph queries for recommendation retrieval." Semantic web 10.6 (2019): 1007-1037.
Mezni, Haithem, Djamal Benslimane, and Ladjel Bellatreche. "Context-aware service recommendation based on knowledge graph embedding." IEEE Transactions on Knowledge and Data Engineering 34.11 (2021): 5225-5238.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN W FIGUEROA whose telephone number is (571)272-4623. The examiner can normally be reached Monday-Friday, 10AM-6PM EST.
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KEVIN W FIGUEROA
Primary Examiner
Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124