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
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-17 are rejected under 35 U.S.C. 101 because they are directed to abstract ideas without significantly more.
Regarding claims 1-17,
Step 1: Applying step 1, the preamble of claims 1-8, 12-13, and 16-17 claims an information processing device, which falls within the statutory category of an apparatus. The preamble of claims 9-10 claims a communication system, which falls within the statutory category of an apparatus. The preamble of claims 11 and 14-15 claims a generation method, which falls within the statutory category of a process.
Regarding claim 1,
Step 2A – Prong One: Claim 1 recites:
An information processing device comprising:
an acquiring circuitry to acquire attribute information regarding a first user, acquire information indicating a user having attribute information similar to the attribute information regarding the first user, and acquire appliance condition information as information regarding condition of an appliance used by the user; [[and]]
a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information;
a behavior identifying circuitry to identify the behavior of the first user based on the first learning model; and
a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind, with or without the physical aids of a pen and paper. A human could use observation and evaluation to acquire attribute information regarding themselves or condition information of the appliance they are using, as in the specification of the claimed invention at [0022] – “Each user inputs information regarding attributes of the user”. A human could use observation and evaluation to acquire information indicating a user having similar attribute information to the attribute information of the first user. A human could use observation and evaluation to identify behavior of the first user.
Step 2A – Prong One (Yes).
Step 2A – Prong Two: The additional elements of the claim are “an acquiring circuitry” and “a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information; based on the first learning model; and a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry”. These additional elements are mere instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim.
Even when viewed in combination, the additional elements do not integrate the judicial exception into practical application.
Step 2A – Prong Two (No).
Step 2B: As explained with respect to Step 2A, the additional elements of the claim are mere instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)) and cannot provide an inventive concept, even when considered in combination. The additional elements do not amount to significantly more than the judicial exception.
Step 2B (No).
Claim 1 is ineligible.
With respect to claims 3, 5, 9, 11, 12, and 14-17,
These claims are similar in scope to claim 1 and are rejected under a similar rationale.
In claim 3, the limitation of “to calculate information regarding a behavior time of the first user based on the user appliance condition information and user sensor information, calculate information regarding the behavior time of each of the plurality of users based on the plurality of pieces of appliance condition information and plurality of pieces of sensor information” under broadest reasonable interpretation is directed to a mathematical concept. Calculating a behavior time of the first user and the plurality of users is a mathematical calculation.
In Claim 3, the limitation of “identify information regarding the behavior time similar to the information regarding the behavior time of each of the plurality of users” under broadest reasonable interpretation is directed to a mathematical concept. Identifying a similar user with a behavior time similar to that of the first user is a mathematical calculation and mathematical relationship, by comparing the calculated behavior times of users.
In claim 3, the further additional elements are “a plurality of pieces of sensor information as information obtained by respectively detecting the plurality of users” and “user sensor information as information obtained by detecting the first user” which are insignificant extra-solution activities that amount to no more than mere data gathering (See MPEP 2106.05(g)). The limitations of “a identifying circuitry”, “by respectively a plurality of sensors”, and “a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the identified information regarding the behavior time, the plurality of pieces of appliance condition information and the plurality of pieces of sensor information” are mere instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim.
In claim 9, the further additional elements are “a first information processing device; and a second information processing device that communicate with the first information processing device, wherein the first processing device includes:” are mere instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). The computer is recited at a high level of generality and imposes no meaningful limitations on the claim.
Even when viewed in combination, these additional elements do not integrate the judicial exception into practical application and do not amount to significantly more than the judicial exception. Data gathering is a well-understood, routine conventional activity as recognized by the courts (See MPEP 2106.05(d)(II)).
Claims 3, 5, 9, 11-12, and 14-17 are ineligible.
Dependent claims:
Claims 2, 6-7, 10, and 13: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
Claims 4 and 8: Recite further insignificant extra-solution activities that amount to no more than mere data gathering and mere instructions to apply the judicial exception on a generic computer. As explained above, these do not provide a practical application or inventive concept and thus are ineligible.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maekawa et al. (NPL: “Unsupervised Activity Recognition with User’s Physical Characteristics Data”, hereinafter “Maekawa”) in view of Liang et al. (NPL: “An Unsupervised User Behavior Prediction Algorithm Based on Machine Learning and Neural Network For Smart Home”, hereinafter “Liang”), and further in view of Zhai et al. (NPL: “Appliance Flexibility Analysis Considering User Behavior in Home Energy Management System Using Smart Plugs”, hereinafter “Zhai”).
Regarding claim 1, Maekawa teaches an information processing device comprising:
an acquiring circuitry to acquire attribute information regarding a first user, acquire information indicating a user having attribute information similar to the attribute information regarding the first user (Maekawa, Section 3.1 Paragraph 1 – “As preparation, we first compute the similarities between the activities of source users by using labeled acceleration data collected from the source users in advance. Second, for each activity class, we learn the relationship between the activity similarities and the attributes of the users’ PC information” – teaches acquiring attribute information regarding a first user (attributes of user’s PC information) and acquires information indicating a user having attribute information similar to the attribute information regarding the first user (learn relationships between activity similarities and attributes of user’s PC information));
Maekawa fails to explicitly teach acquiring appliance condition information as information regarding condition of an appliance used by the user; and a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information.
However, analogous to the field of home and energy management systems, Liang teaches:
acquire appliance condition information as information regarding condition of an appliance used by the user (Liang, Section III Section Algorithm 1 – “dataSet: user’s operation records for a certain smart home device” – teaches acquiring appliance condition information as information regarding condition of an appliance used the user (operation records for a certain smart home device), further supported by Section III A Paragraph 1 – “Our data set is offered by a real in-situ smart home company and the company’s experts have pointed out that the generation date of record, the operation time of device, and the operation state of device are the most important features in this prediction task.”); [[and]]
a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the appliance condition information (behaviors for this device, meaning predictive output is based on the appliance condition information));
identify the behavior of the first user based on the first learning model (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches identifying the behavior of the first user based on the first learning model (model outputs predictive user behaviors for device, thus identifying user behaviors based on the learning model));
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the appliance condition information and learning model of Liang to the attribute information of a first user and similar users of Maekawa in order to create a system that identifies the behavior of a first user based on appliance condition information and similar users. Doing so would improve intelligence in smart homes by making effective recommendations based on user preferences, increasing the income and competitiveness of the business of smart homes (Liang, Introduction Paragraph 4).
The combination of Maekawa and Liang fails to explicitly teach a behavior identifying circuitry; and a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry.
However, analogous to the field of the claimed invention, Zhai teaches:
a behavior identifying circuitry to identify the behavior of the first user (Zhai, Section III, Subsection C, Numbered List Item 2 Paragraph 1 – “The controllability of an appliance in the literature refers to whether the appliance could be controlled without violating its user’s preference, or its user’s comfort level in this paper. The analysis of appliance controllability is based on the user behavior extracted in Section II. Furthermore, we investigate the appliance controllability by dividing it into turn-on controllability (fon) and turn-off controllability (foff), which is similar to shiftable loads and interruptible loads in other works” and in Section III, Subsection C, Numbered List Item 3 Paragraph 1 – “So far we have presented a rather heuristic motivation for the definition of appliance flexibility, which depends on its power consumption and time of the day. For example, if the user is used to take shower before sleep, the power consumption of the electric water heater (EWH) would be high at night; hence, the flexibility of the EWH at night would be high compared with those at other times. Based on the analysis in Section II, the flexibility of an appliance state is relevant to the controllability and power consumption in considered time slot (tstart, tend), which is influenced by user behavior, and the flexibility of an appliance is the combination of the flexibilities of its states.” and Fig. 4 – teaches behavior identifying circuitry to identify the behavior of the first user based on the first learning model (teaches defining appliance flexibility which depends on appliance information and identified user behavior to control appliances based on user behaviors, also in Fig. 4 – shows extracting user behaviors for appliance states to determine appliance inference)); and
a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (Zhai, Section III, Subsection D, Paragraph 1 – “The appliance flexibility could be a reference for appliance scheduling in HEMS under DR applications varying from dayahead DR, such as load shedding, to more lucrative fast ancillary services, such as frequency regulation. In the context of HEMS participating in DR programs, the HEMS receives a DR signal containing duration of a DR event from a utility (such as load aggregator)” and in Paragraph 2 – “the HEMS receives DR event and determines which DR program to be implemented (ADR or not). Then, the HEMS looks for proper appliances to be dispatched as described in Section III-A. Third, the HEMS calculates flexibility of the selected appliances to form a priority list. Finally, the HEMS dispatches appliances based on the order in the priority list” – teaches a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (appliance flexibility, which is based on the identified user behavior as in Section III Subsection C 1-2, is used as reference for appliance scheduling in home energy management systems under demand response applications, thus teaches circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the behavior identification and service execution based on identified behavior of Zhai to the learning models, appliance condition information, and user attribute information of Maekawa and Liang. Doing so would define an appliance flexibility that varies depending on appliance type, electrical characteristics, user behavior, as well as the need of power grid and enable a HEMS unit to determine which and how appliances are dispatched according to a demand response signal based on the flexibility (Zhai, Introduction).
Claims 11 and 12 incorporate all the limitations of claim 1 in a generation method and information processing device and are rejected on the same grounds as above.
Regarding claim 2, the combination of Maekawa, Liang, and Zhai teach the information processing device of claim 1, further comprising a second generating circuitry,
user sensor information as information obtained by detecting the first user by a sensor (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting the target user by a sensor); and
the second generating circuitry generates a second learning model, which identifies the behavior of the first user, by using the second acquisition information and the first learning model (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches generating a second learning model that identifies behavior of the target user by using an initial learning model and second acquisition information).
Maekawa fails to explicitly teach wherein the acquiring circuitry acquires second acquisition information as at least one item of information out of user appliance condition information as information regarding condition of an appliance used by the first user.
However, analogous to the field of home and energy management, Liang teaches:
wherein the acquiring circuitry acquires second acquisition information as at least one item of information out of user appliance condition information as information regarding condition of an appliance used by the first user (Liang, Section III D 2 Paragraph 1 – “The algorithm should realize which records are more important and which should be forgotten. Thus, under the influence of the Ebbinghaus Forgetting Curve, it is considered that the learner should gradually forget the user’s operation records according to its generation date, as like a human would do in order to mine behaviors which are closer to user’s recent behaviors.” – teaches second acquisition information as at least one item of information of user appliance condition information (forgets older behaviors, mines data of behaviors more recent, creating a second acquisition for updating the model) regarding condition of an appliance used by the first users).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the one item of information out of user appliance condition information of Liang to the sensor information and second learning model of Maekawa and Zhai in order to train a second learning model based on second acquisition information comprising appliance condition information and sensor information of the target user. Doing so would address some of the problems which currently exist in traditional algorithms, and allow for easy identification of outliers and out-of-date operation records which may be far from most recent user behaviors (Liang, Section III D)
Regarding claim 3, Maekawa teaches an information processing device comprising:
an acquiring circuitry to acquire attribute information regarding a first user, acquire information indicating a plurality of users having attribute information similar to the attribute information regarding the first user (Maekawa, Section 3.1 Paragraph 1 – “As preparation, we first compute the similarities between the activities of source users by using labeled acceleration data collected from the source users in advance. Second, for each activity class, we learn the relationship between the activity similarities and the attributes of the users’ PC information” – teaches acquiring attribute information regarding a first user (attributes of user’s PC information) and acquires information indicating a user having attribute information similar to the attribute information regarding the first user (learn relationships between activity similarities and attributes of user’s PC information)),
a plurality of pieces of sensor information as information obtained by respectively detecting the plurality of users by respectively a plurality of sensors (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor),
and user sensor information as information obtained by detecting the first user by a sensor (Maekawa, Section 4.2 Paragraph 1 – “We evaluated our recognition method with ‘leave-one-participant-out’ cross validation. That is, we regarded one participant as a target user and remaining participants as source users, and we computed the activity recognition performance of the target user’s sensor data (test data).” – teaches user sensor information as information obtained by detecting the target user by a sensor);
Maekawa fails to explicitly teach acquiring a plurality of pieces of appliance condition information as information regarding condition of appliances respectively used by the plurality of users and acquiring user appliance condition information as information regarding condition of an appliance used by the first user; [[the]] a identifying circuitry to calculate information regarding a behavior time of the first user based on the user appliance condition information and the user sensor information, calculate information regarding the behavior time of each of the plurality of users based on the plurality of pieces of appliance condition information and the plurality of pieces of sensor information, and identify information regarding the behavior time similar to the information regarding the behavior time of the first user out of the information regarding the behavior time of each of the plurality of users; and [[the]] a generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the identified information regarding the behavior time, the plurality of pieces of appliance condition information and the plurality of pieces of sensor information.
However, analogous to the field of home and energy management systems, Liang teaches:
acquire a plurality of pieces of appliance condition information as information regarding condition of appliances respectively used by the plurality of users (Liang, Section III Paragraphs 4-5 – “b) Initialization stage: In UUBP algorithm, the user’s operation records of a certain smart home device will be input into an ANN to execute the initialization stage in order to get the number of clusters and the respective centroid vector of each cluster automatically without manual setting. c) Assignment stage: Assign each data point to the cluster whose centroid vector has the least squared Euclidean distance to this record according to the operation time and the operation state of the smart home device.” – teaches acquiring a plurality of pieces of appliance condition information as information regarding condition of appliances respectively used by the plurality of users (assigns device operation record to a cluster based on Euclidean distance to other device operation records of users))
and acquire user appliance condition information as information regarding condition of an appliance used by the first user (Liang, Section III Section Algorithm 1 – “dataSet: user’s operation records for a certain smart home device” – teaches acquiring appliance condition information as information regarding condition of an appliance used the user (operation records for a certain smart home device), further supported by Section III A Paragraph 1 – “Our data set is offered by a real in-situ smart home company and the company’s experts have pointed out that the generation date of record, the operation time of device, and the operation state of device are the most important features in this prediction task.”)
a calculation identifying circuitry to calculate information regarding a behavior time of the first user based on the user appliance condition information and the user sensor information, calculate information regarding the behavior time of each of the plurality of users based on the plurality of pieces of appliance condition information and the plurality of pieces of sensor information (Liang, Section III Subsection A Paragraph 2 – “In the UUBP algorithm, the generation date of the record, the duration of this activity, and the operation state of device will be mapped to the same format by using Equation (1), (2), and (3):” – teaches equations calculate information regarding a behavior time (date of record, duration of activity, operation state of device) of the first user or a plurality of users based on appliance condition information and sensor information (equations 1, 2, and 3 display operation records representative of behavior time). In addition to the previously cited passages, Liang further teaches in Section III Paragraph 4 – “In UUBP algorithm, the user’s operation records of a certain smart home device will be input into an ANN to execute the initialization stage in order to get the number of clusters and the respective centroid vector of each cluster automatically without manual setting” – teaches user’s operation records of certain smart home devices, wherein the operation records include the generation date of the record, the duration of the activity, and the operation state of the device, thus calculating a behavior time of each of the plurality of users based on appliances that correspond to the user’s operation records), and identify information regarding the behavior time similar to the information regarding the behavior time of the first user out of the information regarding the behavior time of each of the plurality of users (Liang, Section III Paragraph 5 – “c) Assignment stage: Assign each data point to the cluster whose centroid vector has the least squared Euclidean distance to this record according to the operation time and the operation state of the smart home device.” – teaches using Euclidean distance to identify information (a cluster assignment) regarding the behavior time (duration of activity) similar to the behavior time of the first user (point being assigned to a cluster is the target user’s device operation record)); [[and]]
a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the identified information regarding the behavior time, the plurality of pieces of appliance condition information and the plurality of pieces of sensor information (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the appliance condition information (behaviors for this device, meaning predictive output is based on the appliance condition information), and in Section III A Paragraph 2 – “Data transformation allows the mapping of the data from its given format into the format expected by the prediction algorithm. In the UUBP algorithm, the generation date of the record, the duration of this activity, and the operation state of device will be mapped to the same format by using Equation (1), (2), and (3):” – teaches the first learning model identifying behavior of the first user based on behavior time (duration of activity, equation 2));
identify the behavior of the first user based on the first learning model (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches identifying the behavior of the first user based on the first learning model (model outputs predictive user behaviors for device, thus identifying user behaviors based on the learning model));
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the appliance condition information, behavior time, and first learning model of Liang to the sensor data and attribute data of Maekawa in order to predict user behaviors based on behavior times, appliance condition information, and sensor information. Doing so would enable mining the behaviors of users and return it to the smart home control center, where it will intelligently recommend services based on the user behaviors (Liang, Section II).
The combination of Maekawa and Liang fails to explicitly teach a behavior identifying circuitry; and a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry.
However, analogous to the field of the claimed invention, Zhai teaches:
a behavior identifying circuitry to identify the behavior of the first user (Zhai, Section III, Subsection C, Numbered List Item 2 Paragraph 1 – “The controllability of an appliance in the literature refers to whether the appliance could be controlled without violating its user’s preference, or its user’s comfort level in this paper. The analysis of appliance controllability is based on the user behavior extracted in Section II. Furthermore, we investigate the appliance controllability by dividing it into turn-on controllability (fon) and turn-off controllability (foff), which is similar to shiftable loads and interruptible loads in other works” and in Section III, Subsection C, Numbered List Item 3 Paragraph 1 – “So far we have presented a rather heuristic motivation for the definition of appliance flexibility, which depends on its power consumption and time of the day. For example, if the user is used to take shower before sleep, the power consumption of the electric water heater (EWH) would be high at night; hence, the flexibility of the EWH at night would be high compared with those at other times. Based on the analysis in Section II, the flexibility of an appliance state is relevant to the controllability and power consumption in considered time slot (tstart, tend), which is influenced by user behavior, and the flexibility of an appliance is the combination of the flexibilities of its states.” and Fig. 4 – teaches behavior identifying circuitry to identify the behavior of the first user based on the first learning model (teaches defining appliance flexibility which depends on appliance information and identified user behavior to control appliances based on user behaviors, also in Fig. 4 – shows extracting user behaviors for appliance states to determine appliance inference)); and
a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (Zhai, Section III, Subsection D, Paragraph 1 – “The appliance flexibility could be a reference for appliance scheduling in HEMS under DR applications varying from dayahead DR, such as load shedding, to more lucrative fast ancillary services, such as frequency regulation. In the context of HEMS participating in DR programs, the HEMS receives a DR signal containing duration of a DR event from a utility (such as load aggregator)” and in Paragraph 2 – “the HEMS receives DR event and determines which DR program to be implemented (ADR or not). Then, the HEMS looks for proper appliances to be dispatched as described in Section III-A. Third, the HEMS calculates flexibility of the selected appliances to form a priority list. Finally, the HEMS dispatches appliances based on the order in the priority list” – teaches a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (appliance flexibility, which is based on the identified user behavior as in Section III Subsection C 1-2, is used as reference for appliance scheduling in home energy management systems under demand response applications, thus teaches circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the behavior identification and service execution based on identified behavior of Zhai to the learning models, appliance condition information, and user attribute information of Maekawa and Liang. Doing so would define an appliance flexibility that varies depending on appliance type, electrical characteristics, user behavior, as well as the need of power grid and enable a HEMS unit to determine which and how appliances are dispatched according to a demand response signal based on the flexibility (Zhai, Introduction).
Claims 14 and 16 incorporate all the limitations of claim 3 in a generation method and information processing device and are rejected on the same grounds as above.
Regarding claim 4, the combination of Maekawa, Liang, and Zhai teach the information processing device of claim 3, further comprising a second generating circuitry to generate a second learning model, which identifies the behavior of the first user, by using the user sensor information and the first learning model (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches generating a second learning model that identifies behavior of the target user by using an initial learning model and sensor information).
Maekawa fails to teach identifying the behavior of the first user by using the user appliance condition information.
However, analogous to the field of home and energy management systems, Liang teaches:
which identifies the behavior of the first user, by using the user appliance condition information (Liang, Algorithm 1 – “dataSet: user’s operation records for a certain smart home device” – teaches using appliance condition information to generate a learning model, and in Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the appliance condition information),
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the user appliance condition information of Liang to the second model and sensor information of Maekawa and Zhai in order to produce a second learning model that identifies the behavior of the target user by using appliance condition information, sensor information, and the first learning model. Doing so would address some of the problems which currently exist in traditional algorithms, and allow for easy identification of outliers and out-of-date operation records which may be far from most recent user behaviors (Liang, Section III D).
Regarding claim 5, Maekawa teaches an information processing device comprising:
an acquiring circuitry to acquire attribute information regarding a plurality of users, acquire information to which residents having attribute information similar to the attribute information regarding the plurality of users belong (Maekawa, Section 3.1 Paragraph 1 – “As preparation, we first compute the similarities between the activities of source users by using labeled acceleration data collected from the source users in advance. Second, for each activity class, we learn the relationship between the activity similarities and the attributes of the users’ PC information” – teaches acquiring attribute information regarding a first user (attributes of user’s PC information) and acquires information indicating a user having attribute information similar to the attribute information regarding the first user (learn relationships between activity similarities and attributes of user’s PC information)),
and household sensor information as information acquired from a sensor used in the household (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor, where the user may be at home performing home activities detected by the sensor);
Maekawa fails to explicitly teach acquiring attribute information regarding a plurality of users belonging to a first household, acquiring information indicating a household to which residents having attribute information similar to the attribute information regarding the plurality of users belong, and acquiring first household acquisition information as at least one item of information out of household appliance condition information as information regarding condition of an appliance used in the household; and [[the]] a first generating circuitry to generate a first behavior identification learning model, which identifies behavior of at least one user among the plurality of users, based on the first household acquisition information.
However, analogous to the field of home and energy management systems, Liang teaches
an acquiring circuitry to acquire attribute information regarding a plurality of users belonging to a first household (Liang, Section III Paragraph 4 – “b) Initialization stage: In UUBP algorithm, the user’s operation records of a certain smart home device will be input into an ANN to execute the initialization stage in order to get the number of clusters and the respective centroid vector of each cluster automatically without manual setting.” – teaches acquiring user operation records of devices belonging to a smart home, and in Tables 2-4 – a list of all devices and the number of records associated with those devices in a smart home)
acquire information indicating a household to which residents having attribute information similar to the attribute information regarding the plurality of users belong (Liang, Section III Paragraph 5 – “c) Assignment stage: Assign each data point to the cluster whose centroid vector has the least squared Euclidean distance to this record according to the operation time and the operation state of the smart home device.” – teaches using Euclidean distance to assign data records to clusters of similar records, thus acquiring information (a cluster assignment to a cluster of smart home device records) indicating a household to which residents having attribute information similar to the attribute of the residents of the first household)
and acquire first household acquisition information as at least one item of information out of household appliance condition information as information regarding condition of an appliance used in the household (Liang, Section III C Paragraph 1 – “In this stage, the UUBP algorithm will assign each data point (user operation record) to the cluster whose centroid vector has the least squared Euclidean distance to this record. This is according to the operation time and the operation state of the smart home device in this record by using Equation (10)… Here, rp indicates a data point which has two eigenvalues: the operating time and the state of the smart home device in this user operation record.” – teaches acquiring first household acquisition information as at least one item of information out of household appliance condition information (rp indicates a record of device operating time and state of the user operation record)); [[and]]
a first generating circuitry to generate a first behavior identification learning model, which identifies behavior of at least one user among the plurality of users, based on the first household acquisition information (Liang, Section III Paragraphs 6-7 – “d) Update stage: In the UUBP algorithm, a forgetting factor will be integrated to propose a novel update strategy and calculate the new centroid vectors of each cluster in the new clusters. e) User behavior generation: The final centroid vectors have to be transformed to a format which can be comprehended by the user” – teaches a first behavior identification learning model which identifies behavior of at least one user among the plurality of users (proposes forgetting factor update strategy for model to update centroid vectors, and then transforms centroid vectors into format comprehensible by user which represents predicted user behavior), based on the first household acquisition information, as supported in Section III C Paragraph 1 – “In this stage, the UUBP algorithm will assign each data point (user operation record) to the cluster whose centroid vector has the least squared Euclidean distance to this record. This is according to the operation time and the operation state of the smart home device in this record by using Equation (10)… Here, rp indicates a data point which has two eigenvalues: the operating time and the state of the smart home device in this user operation record.” – teaches the first household acquisition information (user operation record, comprising operating time and state of the smart home device in this user operation record));
identify the behavior of the first user based on the first learning model (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches identifying the behavior of the first user based on the first learning model (model outputs predictive user behaviors for device, thus identifying user behaviors based on the learning model));
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the information regarding a plurality of users belonging to a first household and similar household information, appliance condition information, and the first learning model of Liang to the attribute information and sensor information of Maekawa in order to produce a first learning model to identify the behavior of at least one user out of a plurality of users based on the first household acquisition information. Doing so would predict user behaviors, derived from mass historical and real-time user operation records data, aiming to improve the intelligence of the smart home (Liang, Introduction).
The combination of Maekawa and Liang fails to explicitly teach a behavior identifying circuitry; and a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry.
However, analogous to the field of the claimed invention, Zhai teaches:
a behavior identifying circuitry to identify the behavior of the first user (Zhai, Section III, Subsection C, Numbered List Item 2 Paragraph 1 – “The controllability of an appliance in the literature refers to whether the appliance could be controlled without violating its user’s preference, or its user’s comfort level in this paper. The analysis of appliance controllability is based on the user behavior extracted in Section II. Furthermore, we investigate the appliance controllability by dividing it into turn-on controllability (fon) and turn-off controllability (foff), which is similar to shiftable loads and interruptible loads in other works” and in Section III, Subsection C, Numbered List Item 3 Paragraph 1 – “So far we have presented a rather heuristic motivation for the definition of appliance flexibility, which depends on its power consumption and time of the day. For example, if the user is used to take shower before sleep, the power consumption of the electric water heater (EWH) would be high at night; hence, the flexibility of the EWH at night would be high compared with those at other times. Based on the analysis in Section II, the flexibility of an appliance state is relevant to the controllability and power consumption in considered time slot (tstart, tend), which is influenced by user behavior, and the flexibility of an appliance is the combination of the flexibilities of its states.” and Fig. 4 – teaches behavior identifying circuitry to identify the behavior of the first user based on the first learning model (teaches defining appliance flexibility which depends on appliance information and identified user behavior to control appliances based on user behaviors, also in Fig. 4 – shows extracting user behaviors for appliance states to determine appliance inference)); and
a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (Zhai, Section III, Subsection D, Paragraph 1 – “The appliance flexibility could be a reference for appliance scheduling in HEMS under DR applications varying from dayahead DR, such as load shedding, to more lucrative fast ancillary services, such as frequency regulation. In the context of HEMS participating in DR programs, the HEMS receives a DR signal containing duration of a DR event from a utility (such as load aggregator)” and in Paragraph 2 – “the HEMS receives DR event and determines which DR program to be implemented (ADR or not). Then, the HEMS looks for proper appliances to be dispatched as described in Section III-A. Third, the HEMS calculates flexibility of the selected appliances to form a priority list. Finally, the HEMS dispatches appliances based on the order in the priority list” – teaches a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (appliance flexibility, which is based on the identified user behavior as in Section III Subsection C 1-2, is used as reference for appliance scheduling in home energy management systems under demand response applications, thus teaches circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the behavior identification and service execution based on identified behavior of Zhai to the learning models, appliance condition information, and user attribute information of Maekawa and Liang. Doing so would define an appliance flexibility that varies depending on appliance type, electrical characteristics, user behavior, as well as the need of power grid and enable a HEMS unit to determine which and how appliances are dispatched according to a demand response signal based on the flexibility (Zhai, Introduction).
Claims 15 and 17 incorporate all the limitations of claim 5 in a generation method and information processing device and are rejected on the same grounds as above.
Regarding claim 6, the combination of Maekawa, Liang, and Zhai teach the information processing device according to claim 5, further comprising a second generating circuity, wherein
user household sensor information as information acquired from a sensor used in the first household (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor, where the user may be at home performing home activities detected by the sensor),
and the second generating circuitry generates a second behavior identification learning model, which identifies the behavior of at least one user among the plurality of users, by using the second household acquisition information and the first behavior identification learning model (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches generating a second learning model that identifies behavior of the target user by using an initial learning model and second acquisition information (adapt models to recognition results to achieve more exact adaption)).
Maekawa fails to explicitly teach the acquiring circuitry acquires second household acquisition information as at least one item of information out of user household appliance condition information as information regarding condition of an appliance used in the first household.
However, analogous to the field of home and energy management systems, Liang teaches:
the acquiring circuitry acquires second household acquisition information as at least one item of information out of user household appliance condition information as information regarding condition of an appliance used in the first household (Liang, Section III D 2 Paragraphs 1-2 – “Thus, under the influence of the Ebbinghaus Forgetting Curve, it is considered that the learner should gradually forget the user’s operation records according to its generation date, as like a human would do in order to mine behaviors which are closer to user’s recent behaviors. So, it is proposed a forgetting factor model to complete this task is defined in Equation (12): ω(ri) = exp(− date(ri) / pi ) (12) Here, ri indicates a certain data point in this cluster which is a user operation record. pi indicates the probability parameters of this record ri , it is proposed to promote the convergence of the clustering process and magnitude the difference between each record to improve the importance of generation date during the prediction clustering” – teaches acquiring second household acquisition information (a certain operation record in the cluster, weighted depending on how recent the behavior occurred) as at least one item of information out of user household appliance condition information (the most recent behavior of the target user is acquired and weighted accordingly for use in later processing))
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the second household acquisition information of Liang to the second learning model and sensor data of Maekawa and Zhai in order to create a second learning model using second household acquisition information comprising household appliance condition information and sensor information of the target user. Doing so would enable the system to represent the fact that user behaviors will change over time and a user behavior prediction algorithm should realize which records are most important and which should be forgotten (Liang, Section III D 2).
Regarding claim 7, the combination of Maekawa, Liang, and Zhai teaches the information processing device according to claim 5, further comprising a calculation identifying circuitry,
wherein the acquiring circuitry acquires information indicating a plurality of residents having attribute information similar to the attribute information regarding the plurality of users (Maekawa, Section 3.1 Paragraph 1 – “As preparation, we first compute the similarities between the activities of source users by using labeled acceleration data collected from the source users in advance. Second, for each activity class, we learn the relationship between the activity similarities and the attributes of the users’ PC information” – teaches acquiring attribute information regarding a first user (attributes of user’s PC information) and acquires information indicating users having attribute information similar to the attribute information regarding the first user (learn relationships between activity similarities and attributes of user’s PC information)),
and a plurality of pieces of household sensor information as information acquired from a sensor used in each of the plurality of households (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor, where the users may be at home performing home activities detected by the sensor),
and user household sensor information as information acquired from a sensor used in the first household (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor, where target the user may be at home performing home activities detected by the sensor),
Maekawa fails to explicitly teach acquiring information indicating a plurality of households corresponding to a plurality of residents having attribute information similar to the plurality of users, acquiring a plurality of pieces of household appliance condition information as information regarding condition of an appliance used in each of the plurality of households, acquiring user household appliance condition information as information regarding condition of an appliance used in the first household, the calculation identifying circuitry calculates information regarding a behavior time of the plurality of users based on the user household appliance condition information and the user household sensor information, calculates information regarding the behavior time of each of the plurality of residents based on the plurality of pieces of household appliance condition information and the plurality of pieces of household sensor information, identifies information regarding the behavior time similar to the information regarding the behavior time of the plurality of users in the information regarding the behavior time of each of the plurality of residents, and the first generating circuitry generates the first behavior identification learning model based on the identified information regarding the behavior time, the plurality of pieces of household appliance condition information and the plurality of pieces of household sensor information.
However, analogous to the field of home and energy management systems, Liang teaches:
acquires information indicating a plurality of households corresponding to a plurality of residents having attribute information similar to the attribute information regarding the plurality of users (Liang, Section III Paragraph 5 – “c) Assignment stage: Assign each data point to the cluster whose centroid vector has the least squared Euclidean distance to this record according to the operation time and the operation state of the smart home device.” – teaches using Euclidean distance to assign data records to clusters of similar records, thus acquiring information (a cluster assignment to a cluster of smart home device records) indicating households to which residents having attribute information similar to the attribute of the plurality of users)
acquires a plurality of pieces of household appliance condition information as information regarding condition of an appliance used in each of the plurality of households (Liang, Section III C Paragraph 1 – “In this stage, the UUBP algorithm will assign each data point (user operation record) to the cluster whose centroid vector has the least squared Euclidean distance to this record. This is according to the operation time and the operation state of the smart home device in this record by using Equation (10)… Here, rp indicates a data point which has two eigenvalues: the operating time and the state of the smart home device in this user operation record.” – teaches acquiring a plurality of pieces of household appliance condition information as information regarding condition of an appliance used in each of the plurality of households (a cluster of users operation records for smart home devices))
and acquires user household appliance condition information as information regarding condition of an appliance used in the first household (Liang, Section III D 2 Paragraphs 1-2 – “Thus, under the influence of the Ebbinghaus Forgetting Curve, it is considered that the learner should gradually forget the user’s operation records according to its generation date, as like a human would do in order to mine behaviors which are closer to user’s recent behaviors. So, it is proposed a forgetting factor model to complete this task is defined in Equation (12): ω(ri) = exp(− date(ri) / pi ) (12) Here, ri indicates a certain data point in this cluster which is a user operation record. pi indicates the probability parameters of this record ri , it is proposed to promote the convergence of the clustering process and magnitude the difference between each record to improve the importance of generation date during the prediction clustering” – teaches household appliance condition information as information regarding condition of an appliance used in the first household (most recent target user operation record))
the calculation identifying circuitry calculates information regarding a behavior time of the plurality of users based on the user household appliance condition information and the user household sensor information, calculates information regarding the behavior time of each of the plurality of residents based on the plurality of pieces of household appliance condition information and the plurality of pieces of household sensor information (Liang, “In the UUBP algorithm, the generation date of the record, the duration of this activity, and the operation state of device will be mapped to the same format by using Equation (1), (2), and (3):” – teaches equations calculate information regarding a behavior time (date of record, duration of activity) of the first user or a plurality of users based on household appliance condition information), and identifies information regarding the behavior time similar to the information regarding the behavior time of the plurality of users in the information regarding the behavior time of each of the plurality of residents (Liang, Section III Paragraph 5 – “c) Assignment stage: Assign each data point to the cluster whose centroid vector has the least squared Euclidean distance to this record according to the operation time and the operation state of the smart home device.” – teaches using Euclidean distance to identify information (a cluster assignment) regarding the behavior time of each user (duration of activity for each record) similar to the behavior time of the target (point being assigned to a cluster is the target user’s device operation record)), and
the first generating circuitry generates the first behavior identification learning model based on the identified information regarding the behavior time, the plurality of pieces of household appliance condition information and the plurality of pieces of household sensor information (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the household appliance condition information (predictive output is based on the device information), and in Section III A Paragraph 2 – “Data transformation allows the mapping of the data from its given format into the format expected by the prediction algorithm. In the UUBP algorithm, the generation date of the record, the duration of this activity, and the operation state of device will be mapped to the same format by using Equation (1), (2), and (3):” – teaches the first learning model identifying behavior of the first user based on behavior time (duration of activity, equation 2)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the information regarding a plurality of households corresponding to a plurality of residents having information similar to that of the plurality of users, household appliance condition information, behavior time, and first learning model of Liang to the household sensor data and attribute data of Maekawa and Zhai in order to predict user behaviors based on behavior times, household appliance condition information, and household sensor information. Doing so would enable mining the behaviors of users and return it to the smart home control center, where it will intelligently recommend services based on the user behaviors (Liang, Section II).
Regarding claim 8, the combination of Maekawa, Liang, and Zhai teach the information processing device according to claim 7, further comprising a second generating circuitry to generate a second behavior identification learning model, which identifies the behavior of at least one user among the plurality of users, by using the user household sensor information and the first behavior identification learning model (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches generating a second learning model that identifies behavior of the target user by using an initial learning model and household sensor information, where the sensor may detect the user’s activities within their home).
Maekawa fails to explicitly teach identifying the behavior of at least one user among the plurality of users by using the household appliance condition information.
However, analogous to the field of home and energy management systems, Liang teaches:
identifies the behavior of at least one user among the plurality of users, by using the user household appliance condition information (Liang, Section III Paragraph 4 – “b) Initialization stage: In UUBP algorithm, the user’s operation records of a certain smart home device will be input into an ANN to execute the initialization stage in order to get the number of clusters and the respective centroid vector of each cluster automatically without manual setting.” – teaches acquiring household appliance condition information (operation records of a smart home device), and in Tables 2-4 – a list of all devices and the number of record associated with those devices in a smart home and in Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the household appliance condition information)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the user household appliance condition information of Liang to the second model and sensor information of Maekawa and Zhai in order to produce a second learning model that identified behavior of the target user by using household appliance condition information, household sensor information, and the first learning model. Doing so would address some of the problems which currently exist in traditional algorithms, and allow for easy identification of outliers and out-of-date operation records which may be far from most recent user behaviors (Liang, Section III D).
Regarding claim 9, Maekawa teaches a communication system comprising:
a first information processing device (Maekawa, Section 4.1 Paragraph 1 – “The web camera was connected to a mobile computer carried by the companion” – teaches a first information processing device (camera carried by companion acquiring observed data), and in Section 4.1 Paragraph 1 – “The sensor data from the four sensor nodes attached to the participant were also sent to the mobile computer.” – a first information processing device (the first information processing device may be a sensor)); and
a second information processing device that communicates with the first information processing device (Maekawa, Section 4.1 Paragraph 1 – “The web camera was connected to a mobile computer carried by the companion” – teaches the second information processing device (the mobile computer performing further processes on acquired information from first device)), wherein the first information processing device includes:
an acquiring circuitry to acquire attribute information regarding a first user, acquire information indicating a user having attribute information similar to the attribute information regarding the first user (Maekawa, Section 3.1 Paragraph 1 – “As preparation, we first compute the similarities between the activities of source users by using labeled acceleration data collected from the source users in advance. Second, for each activity class, we learn the relationship between the activity similarities and the attributes of the users’ PC information” – teaches acquiring attribute information regarding a first user (attributes of user’s PC information) and acquires information indicating a user having attribute information similar to the attribute information regarding the first user (learn relationships between activity similarities and attributes of user’s PC information)),
Maekawa fails to explicitly teach acquiring appliance condition information as information regarding condition of an appliance used by the user; and a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information.
However, analogous to the field of home and energy management systems, Liang teaches:
acquire appliance condition information as information regarding condition of an appliance used by the user (Liang, Section III Section Algorithm 1 – “dataSet: user’s operation records for a certain smart home device” – teaches acquiring appliance condition information as information regarding condition of an appliance used the user (operation records for a certain smart home device), further supported by Section III A Paragraph 1 – “Our data set is offered by a real in-situ smart home company and the company’s experts have pointed out that the generation date of record, the operation time of device, and the operation state of device are the most important features in this prediction task.”); [[and]]
a first generating circuitry to generate a first learning model, which identifies behavior of the first user, based on the appliance condition information (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the appliance condition information (behaviors for this device, meaning predictive output is based on the appliance condition information));
identify the behavior of the first user based on the first learning model (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches identifying the behavior of the first user based on the first learning model (model outputs predictive user behaviors for device, thus identifying user behaviors based on the learning model));
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the appliance condition information and learning model of Liang to the attribute information of a first user and similar users of Maekawa in order to create a system that identifies the behavior of a first user based on appliance condition information and similar users. Doing so would improve intelligence in smart homes by making effective recommendations based on user preferences, increasing the income and competitiveness of the business of smart homes (Liang, Introduction Paragraph 4).
The combination of Maekawa and Liang fails to explicitly teach a behavior identifying circuitry; and a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry.
However, analogous to the field of the claimed invention, Zhai teaches:
a behavior identifying circuitry to identify the behavior of the first user (Zhai, Section III, Subsection C, Numbered List Item 2 Paragraph 1 – “The controllability of an appliance in the literature refers to whether the appliance could be controlled without violating its user’s preference, or its user’s comfort level in this paper. The analysis of appliance controllability is based on the user behavior extracted in Section II. Furthermore, we investigate the appliance controllability by dividing it into turn-on controllability (fon) and turn-off controllability (foff), which is similar to shiftable loads and interruptible loads in other works” and in Section III, Subsection C, Numbered List Item 3 Paragraph 1 – “So far we have presented a rather heuristic motivation for the definition of appliance flexibility, which depends on its power consumption and time of the day. For example, if the user is used to take shower before sleep, the power consumption of the electric water heater (EWH) would be high at night; hence, the flexibility of the EWH at night would be high compared with those at other times. Based on the analysis in Section II, the flexibility of an appliance state is relevant to the controllability and power consumption in considered time slot (tstart, tend), which is influenced by user behavior, and the flexibility of an appliance is the combination of the flexibilities of its states.” and Fig. 4 – teaches behavior identifying circuitry to identify the behavior of the first user based on the first learning model (teaches defining appliance flexibility which depends on appliance information and identified user behavior to control appliances based on user behaviors, also in Fig. 4 – shows extracting user behaviors for appliance states to determine appliance inference)); and
a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (Zhai, Section III, Subsection D, Paragraph 1 – “The appliance flexibility could be a reference for appliance scheduling in HEMS under DR applications varying from dayahead DR, such as load shedding, to more lucrative fast ancillary services, such as frequency regulation. In the context of HEMS participating in DR programs, the HEMS receives a DR signal containing duration of a DR event from a utility (such as load aggregator)” and in Paragraph 2 – “the HEMS receives DR event and determines which DR program to be implemented (ADR or not). Then, the HEMS looks for proper appliances to be dispatched as described in Section III-A. Third, the HEMS calculates flexibility of the selected appliances to form a priority list. Finally, the HEMS dispatches appliances based on the order in the priority list” – teaches a service executing circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry (appliance flexibility, which is based on the identified user behavior as in Section III Subsection C 1-2, is used as reference for appliance scheduling in home energy management systems under demand response application, thus teaches circuitry to control the appliance based on the behavior of the first user identified by the behavior identifying circuitry)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the behavior identification and service execution based on identified behavior of Zhai to the learning models, appliance condition information, and user attribute information of Maekawa and Liang. Doing so would define an appliance flexibility that varies depending on appliance type, electrical characteristics, user behavior, as well as the need of power grid and enable a HEMS unit to determine which and how appliances are dispatched according to a demand response signal based on the flexibility (Zhai, Introduction).
Regarding claim 10, the combination of Maekawa, Liang, and Zhai teach the communication system according to claim 9, wherein the first information processing device further includes a second generating circuitry,
the acquiring circuitry acquires second acquisition information as user sensor information as information obtained by detecting the first user by a sensor (Maekawa, Section 4.1 Paragraph 1 - “We collected sensor data with our developed wireless sensor nodes equipped with three-axis acceleration sensors and sampling rates of 30Hz. Each participant wore the sensor nodes on the wrists of both hands, waist, and right thigh” – teaches acquiring sensor information as information obtained by detecting a plurality of users using a sensor, where target the user may be at home performing home activities detected by the sensor),
the second generating circuitry generates a second learning model, which identifies the behavior of the first user, by using the second acquisition information and the first learning model (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches generating a second learning model that identifies behavior of the target user by using an initial learning model and second acquisition information (adapt models to recognition results to achieve more exact adaption)), and
Maekawa fails to explicitly teach the acquiring circuitry acquires second acquisition information as at least one item of information out of user appliance condition information as information regarding condition of an appliance used by the first user, at least one item of information out of the information regarding the condition of the appliance used by the first user.
However, analogous to the field of home and energy management systems, Liang teaches:
the acquiring circuitry acquires second acquisition information as at least one item of information out of user appliance condition information as information regarding condition of an appliance used by the first user (Liang, Section III D 2 Paragraph 1 – “The algorithm should realize which records are more important and which should be forgotten. Thus, under the influence of the Ebbinghaus Forgetting Curve, it is considered that the learner should gradually forget the user’s operation records according to its generation date, as like a human would do in order to mine behaviors which are closer to user’s recent behaviors.” – teaches second acquisition information as at least one item of information of user appliance condition information (forgets older behaviors, mines data of behaviors more recent, creating a second acquisition for updating the model) regarding condition of an appliance used by the first users)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the second acquisition information and appliance condition information of Liang to the sensor information and second learning model based on an initial learning model of Maekawa and Zhai in order to predict user behaviors based on appliance condition information, sensor information, and one of either the first or second models. Doing so would provide ambient intelligence to provide context-aware services and facilitate remote home control (Liang, Introduction) and provide more personalized service which can be improved by the user experiences (Liang, Introduction).
Regarding claim 13, the combination of Maekawa, Liang, and Zhai teach the information processing device according to claim 1,
sensor information as information obtained by detecting the user by a sensor (Maekawa, Section 3.4 Paragraph 3 – “Since the test data of the target user are unlabeled, we first recognize the test data with the initial models and then adapt the models according to the recognition results to achieve a more exact adaptation.” – teaches information obtained by detecting the user by sensor),
Maekawa fails to explicitly teach wherein the acquiring circuitry acquires first acquisition information as the appliance condition information and the first generating circuitry that generates the first learning model based on the first acquisition information.
However, analogous to the field of home and energy management systems, Liang teaches:
wherein the acquiring circuitry acquires first acquisition information as the appliance condition information (Liang, Section III Section Algorithm 1 – “dataSet: user’s operation records for a certain smart home device” – teaches acquiring appliance condition information as information regarding condition of an appliance used the user (operation records for a certain smart home device), further supported by Section III A Paragraph 1 – “Our data set is offered by a real in-situ smart home company and the company’s experts have pointed out that the generation date of record, the operation time of device, and the operation state of device are the most important features in this prediction task.”)
and the first generating circuitry that generates the first learning model based on the first acquisition information (Liang, Section III Section Algorithm 1 – “Output: the predictive user behaviors for this device.” – teaches a probability model that outputs predictive user behaviors based on the first acquisition information (behaviors for this device, meaning predictive output is based on the appliance condition information)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the acquisition information comprising appliance condition information and first learning model of Liang to the sensor information of Maekawa and Zhai in order to create a first learning model that identifies user behaviors based on first acquisition information comprising appliance condition information and sensor information. Doing so would improve intelligence in smart homes by making effective recommendations based on user preferences, increasing the income and competitiveness of the business of smart homes (Liang, Introduction Paragraph 4).
Response to Arguments
Applicant's arguments filed 23 September 2025 have been fully considered but they are not persuasive. Claims 1-17 is/are rejected under 35 U.S.C. 101 as the claims recited abstract ideas and do not amount to significantly more than the judicial exception. The limitations of claim 1 regarding “to acquire attribute information regarding a first user, acquire information indicating a user having attribute information similar to the attribute information regarding the first user, and acquire appliance condition information as information regarding condition of an appliance used by the user” are directed to a mental process able to be performed in the human mind, with or without the physical aids of a pen and paper. Claim 1 does not contain any additional elements that would integrate the judicial exception into practical application and does not contain any additional elements that would amount to significantly more than the judicial exception. The additional elements of claim 1 are mere instructions to apply the judicial exception on a generic computer (See MPEP 2106.05(f)). For further details, please see the revised 35 U.S.C. 101 rejection above.
Applicant argues on pp. 2 of Remarks that the features recited in amended claim 1 overcome the limitations of conventional systems by limiting the data used to train the first learning model to data that is actually relevant to appliance control, identifying the behavior of the user, and controlling the appliance based on the behavior of the user. Applicant further argues that the claims of the instant application integrate any abstract idea upon which they might touch into a practical application, namely energy management and appliance control. Examiner respectfully disagrees. The claims of the instant application do not reflect the purported improvement of limiting data used to train the first learning model to data that is relevant to appliance control. Further, in MPEP 2106.05(f), “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".” As in the instant application in claim 1, the limitations attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. Instead, claim 1 acquires information regarding a first user, acquires information regarding a user having similar attribute information to the first user, and acquires appliance condition information. Then, a first learning model is generated, a behavior is identified, and an appliance is controlled based on the identified behavior. The amended limitations of claim 1 do not integrate the judicial exception into a practical application and does not provide significantly more because this type of recitation is equivalent to the words “apply it”.
Applicant’s arguments, see pp. 3-4 of Remarks, filed 23 September 2025, with respect to the rejection(s) of claim(s) 1-17 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Maekawa in view of Liang, further in view of Zhai et al. (NPL: “Appliance Flexibility Analysis Considering User Behavior in Home Energy Management System Using Smart Plugs”). Maekawa teaches the limitations of claim 1 regarding “an acquiring circuitry to acquire attribute information…”. Liang teaches the limitations of claim 1 regarding “acquire appliance condition information…” and “a first generating circuitry…”. Zhai teaches the amended limitations of claim 1 regarding “a behavior identifying circuitry…” and “a service execution circuitry…”.
On pp. 3 of Remarks, Applicant argues regarding claim 3 that Liang fails to teach calculating information regarding a behavior time of each of the plurality of users. Examiner respectfully disagrees. Upon reference to the specification of the claimed invention at [0089] – “information regarding the behavior time is information that is difficult for a resident to input correctly by using the input device 240. For example, the information regarding the behavior time is wake-up time, bedtime, meal time, presence in a room or the like” and in [0090] – “calculation identification unit 190 calculates information regarding the behavior time of each of the plurality of users corresponding to the plurality of user IDs based on a plurality of pieces of appliance condition information corresponding to the plurality of user IDs”. Liang teaches in Section III Paragraph 4 – “In UUBP algorithm, the user’s operation records of a certain smart home device will be input into an ANN to execute the initialization stage in order to get the number of clusters and the respective centroid vector of each cluster automatically without manual setting” and in Section III Subsection A Paragraph 2 – “In the UUBP algorithm, the generation date of the record, the duration of this activity, and the operation state of device will be mapped to the same format by using Equation (1), (2), and (3):” that the user’s operation records include the generation date of the record, the duration of the activity, and the operation state of the device, according to 3 equations that utilize date, time, and state information, thus determining a behavior time of each of the plurality of users based on a plurality of pieces of appliance condition information corresponding to the plurality of users.
On pp. 3 of Remarks, applicant argues regarding claim 5 that Maekawa does not teach acquiring attribute information regarding a plurality of users belonging to a first household. Examiner notes that Maekawa is cited as teaching acquiring attribute information regarding a plurality of users. Liang teaches “an acquiring circuitry to acquire attribute information regarding a plurality of users belonging to a first household…”. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOUIS C NYE whose telephone number is 571-272-0636. The examiner can normally be reached Monday - Friday 9:00AM - 5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MATT ELL can be reached at 571-270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LOUIS CHRISTOPHER NYE/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141