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 .
Status of Claims
Claims 1-21 are pending and are examined herein.
Claims 1-21 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 1-21 are rejected under 35 USC 103.
Response to Arguments
Applicant's arguments with respect to the 35 USC 101 rejection have been fully considered but they are not persuasive.
On pages 10-11, applicant argues:
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Examiner respectfully disagrees. First, it is noted that the steps of receiving data, generating an intent model, using the generated model to detect a current state, and transmitting instructions to a device are analyzed as additional element. However, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more (see updated 35 USC 101 rejections in response to the claim amendments).
Next, the argument that the determination of a target state of a user and facilitating transitioning of a user conserves the computing resources is also not persuasive. It is noted that the application’s specification at [0011] states that the feature of the intervention model learning over time can reduce the amount of computational resources required to achieve transitions. The improvement of reducing computational resources is directed to the well-known concept of iteratively training of machine learning models, which improves the accuracy of model predictions over multiple iterations (i.e. learn over time that certain sequences are more likely to result in a target state than others).
On page 11, applicant argues:
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Examiner disagrees. The steps of using a sequence of actions to guide the user into a new states is reasonably interpreted as a series of steps that includes observations and determinations, which are mental processes. For example, a human can identify a set of candidate states based on a user’s current state, determine a target state for the user, and select a next state for the user base on the next state’s probability to achieve a target state for the user. The additional features of using sensors and a generating machine learning model to determine a state of the user and transmitting instructions to initiate action for the user to transition to the next state are mere attempts to perform the mental process on generic computer components.
Applicant’s arguments with respect to the prior art rejection of claim(s) 1-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101 - Abstract Idea
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 1-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis
According to the first part of the analysis, in the instant case Claims 1-7 are directed to a method, Claims 8-14 are directed to an apparatus, and Claims 15-21 are directed to a non-transitory medium that stores the method; consequently, these claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis (Combined Step 2A Prong 1-2 and Step 2B Analysis)
Claim 1 includes the following recitation of an abstract idea:
detecting, by the generated intent model and based on the data received from the plurality of sensors, a current state of a user (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. The use of an intent model to perform the detection is directed to mere instructions to apply an abstract idea on a generic computer. The additional limitation does not integrate the judicial exception into a practical application and does not amount to significantly more than the judicial exception, see MPEP 2106.05(f))
identifying, based on the current state of the user, a set of candidate states to which the user can transition from the current state (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
selecting, based on one or more of the data received from the plurality of sensors or the current state of the user, a target state for the user (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
for each of the set of candidate states, determining a probability at which the user will transition from the current state to the target state through at least a respective candidate state of the set of candidate states, wherein the probability for the transition from the current state to the target state through the at least respective candidate state is determined based on historical behavior data of users indicating a frequency at which the users transitioned from the at least respective candidate state to the target state after transitioning from the current state to the at least respective candidate state; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. Determining the probability could also fall under being a mathematical concept. The amended limitations further describes data used to determine probability (i.e. probability is bas on frequence of state transitions based user history data. These features are also part of the mental process of determining a probability at which the user will transition from one state to another.)
determining initiation of the transition from the current state to the target state based on at least one probability of the determined probabilities exceeding a specified threshold value (This step is directed to the mental process whether state transition should be initiated or not based on comparison of the probability to a threshold. This is practical to perform in the human mind under its broadest reasonable interpretation);
selecting, based on the determination of the initiation of the transition from the current state to the target state, a next state for the user (This step is directed to the mental process of selecting a next state based on the previous determination step. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
determining one or more actions to transition the user from the current state to the next state (This step is directed to the mental process of identifying corresponding actions to transition the user to the next state. This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
generating an intent model by training a machine learning model based on training data collected from a plurality of sensors (The limitation generically covers training a machine learning model using data collected from a plurality of sensors. This falls under mere instructions to apply the abstract idea on a generic computer, see See MPEP 2106.05(f));
receiving data from the plurality of sensors via a communication network (This falls under data transmission, which is a well‐understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i));
initiating the one or more actions, wherein the one or more actions include transmitting instructions to at least one device via the communication network (This falls under mere instructions to apply the actions, see MPEP 2106.05(f) and transmitting instructions via the communication network is directed to a well‐understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i)). Claim 1 does not reflect an improvement to computer technology or any other technology.
Claim 2 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 2 includes the following recitation of an additional abstract idea:
selecting a particular state for which a probability of the user transitioning from the current state to the particular state is less than a threshold (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 2 recites no further additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas. Claim 2 does not reflect an improvement to computer technology or any other
technology.
Claim 3 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 3 includes the following recitation of an additional abstract idea:
selecting a state that is absent from the set of candidate states (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 3 recites no further additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas. Claim 3 does not reflect an improvement to computer technology or any other
technology.
Claim 4 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 4 includes the following recitation of an additional abstract idea:
determining a probability at which the user will transition from the current state to the target state through a sequence of candidate states including the at least respective candidate state (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components. Determining the probability could also fall under being a mathematical concept.)
Claim 4 recites no further additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas. Claim 4 does not reflect an improvement to computer technology or any other
technology.
Claim 5 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 5 includes the following recitation of an additional abstract idea:
determining, based on updated data received from the plurality of sensors, that the user has transitioned from the current state to the next state (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
updating the probability for each candidate state based at least on the next state (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
selecting an additional next state based on the updated probabilities (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 5 recites the following additional elements which, considered individually and as
an ordered combination, do not integrate the abstract idea into a practical application or amount
to significantly more than the abstract idea: initiating one or more additional actions to transition the user from the next state to the additional next state (This falls under mere instructions to apply the actions. See MPEP 2106.05(f).) Claim 5 does not reflect an improvement to computer technology or any other technology.
Claim 6 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 6 includes the following recitation of an additional abstract idea: after initiating the one or more actions, determining, based on updated data received from the plurality of sensors, that the user is performing actions to prevent the transition to the next state (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 6 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: in response to determining that the user is performing actions to prevent the transition to the next state, stopping the one or more actions or performing one or more additional actions to maintain the user in the current state (This falls under mere instructions to apply the actions. See MPEP 2106.05(f).) Claim 6 does not reflect an improvement to computer technology or any other technology.
Claim 7 recites at least the abstract idea identified above in the claim upon which it
depends.
Claim 7 includes the following recitation of an additional abstract idea:
determining to transition the user to the target state based at least on the data received from the plurality of sensors (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.) Claim 7 recites no further additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, that integrate the abstract idea into a practical application or amount to significantly more than the abstract ideas. Claim 7 does not reflect an improvement to computer technology or any other technology.
Claim 8 recites at least the abstract idea identified above in Claim 1. Claim 8 recites substantially similar subject matter to Claims 1 except it is a computer system that performs the method instead of being the method itself, respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 8 recites the following additional elements aside from those described which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform operations (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea and falls under mere instructions to apply. See MPEP 2106.05(f).) Claim 8 does not reflect an improvement to computer technology or any other technology.
Claims 9-14 recite at least the abstract idea identified above in the claim upon which they depend. Claims 9-14 recite substantially similar subject matter to Claims 2-7, respectively, and are rejected with the same rationale, mutatis mutandis. Claims 9-14 do not reflect an improvement to computer technology or any other technology.
Claim 15 recites at least the abstract idea identified above in Claim 1. Claim 15 recites substantially similar subject matter to Claims 1 except it is a computer readable medium that stores the method instead of being the method itself, respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 15 recites the following additional elements aside from those described which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea: A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations (This is a high-level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea and falls under mere instructions to apply. See MPEP 2106.05(f).) Claim 15 does not reflect an improvement to computer technology or any other technology.
Claims 16-21 recite at least the abstract idea identified above in the claim upon which
they depend. Claims 16-21 recite substantially similar subject matter to Claims 2-7, respectively, and are rejected with the same rationale, mutatis mutandis. Claims 16-21 do not reflect an improvement to computer technology or any other technology.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C.
102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the
statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a
new ground of rejection if the prior art relied upon, and the rationale supporting the rejection,
would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 2020/0302310 A1 (hereinafter Woiceshyn) in view of US 2019/0295004 A1 (hereinafter Chaturapruek).
Regarding Claim 1, Woiceshyn teaches
generating an intent model by training a machine learning model (paragraph [0025], “To determine the current user state and the current environment context, the action generation module 104 can include any combination of machine-learning algorithms”) based on training data collected from a plurality of sensors (paragraph [0025], “The action generation module 104 can analyze the sensor data 108 and the environment data 110 to determine a current user state 116 and a current environment context 118”);
receiving data from the plurality of sensors via a communication network (paragraphs [0031] and [0075], “network system 204 can receive the uploaded sensor data 108 as input to the action generation server application 210”);
detecting, by the generated intent model and based on the data received from the plurality of sensors, a current state of a user (paragraphs [0027] and [0077], “For example, the action generation module 104 can apply machine-learning algorithm(s) to identify particular user state parameter(s) and/or environment context parameter(s) with a higher probability and/or correlation to causing the change in mood relative to other user state parameters and/or environment context parameters(s)”);
identifying, based on the current state of the user, a set of candidate states to which the user can transition from the current state (paragraphs [0063]-[0065], “In the environment 600-3, the action generation module 104 generates action suggestion 612 based on the correlation 516. and the related mood changes and environment context parameters as described herein. Thus, the action generation module can analyze past sequences of user mood changes and the correlated past sequence of environment context parameters to generate an action suggestion”);
selecting, based on one or more of the data received from the plurality of sensors or the current state of the user, a target state for the user (paragraph [0032], “Thus. the action generation server application 210 implemented at the network system 204 can detect a change in user mood, and generate action suggestions that are communicated to the user of the computing device 202, where the action suggestions are intended to address the change in user mood, such as action suggestions directed towards modifying a discontented mood to a contented mood”);
for each of a plurality of candidate states, determining a probability at which the user will transition from the current state to the target state through at least a respective candidate state of the set of candidate states (paragraph [0066], “In the example environment 700-2, the aggregated data included in the user history data 120 is generally denoted as timeline 710. The action generation module can analyze the user history data to find a point in time that corresponds to a positive and/or contented user mood, such as by analyzing the user state parameters at varying points in time. At time 712. the action generation module determines that user state parameter 714 corresponds to a positive and/or contented mood state. In response to finding the positive and/or contented mood state, the action generation module analyzes environment context parameters at the time 712 to identify an environmental difference that has a high probability of causing a change in user mood”, emphasis added. See also [0027] and [0028]);
determining initiation of the transition from the current state to the target state based on at least one probability of the determined probabilities exceeding a specified threshold value (paragraph [0028], “In response to identifying the particular user state parameter(s) and/or particular environment context parameter(s) with the higher probability and/or correlation to causing the change in mood, the action generation module 104 generates an action suggestion 128 that is intended to modify the current user state 116”);
selecting, based on the determination of the initiation of the transition from the current state to the target state, a next state for the user (paragraph [0059], “[0059] At time 514, the action generation module 104 identifies a correlation 516 between an excited user mood and an exercise activity. The action generation module can also determine that the excited user mood corresponds to a change in user mood state that occurs at a duration 518 after the lethargic user mood at time 508. In implementations, the action generation module 104 identifies the changes in user moods between the time 504, the time 508, and the time 514 as a sequence of user mood state changes”);
determining one or more actions to transition the user from the current state to the next state (paragraph [0085], “At 1012, an action suggestion is generated based on the user state parameters or the environment context parameters, where the action suggestion is intended to modify the current user state”); and
initiating the one or more actions, wherein the one or more actions include transmitting instructions to at least one device via the communication network (paragraph [0032], “Notably, the server computing device 206 can communicate the action suggestion 128 from the network system 204 to the computing device 202 via the network 114, as indicated at 216, where the action suggestion 128 can be displayed for the user, such as in a device user interface”).
Although Woiceshyn teaches analyzing environment context parameters to identify an environmental difference that has a high probability of causing a change in user mood and suggesting an action, Woiceshyn does not specification teach:
wherein the probability for the transition from the current state to the target state through the at least respective candidate state is determined based on historical behavior data of users indicating a frequency at which the users transitioned from the at least respective candidate state to the target state after transitioning from the current state to the at least respective candidate state.
However, Chaturapruek teaches using a transition model to provide recommended action (Fig. 5, step 508, see also paragraph [0065]), wherein the probability for the transition from the current state to the target state through the at least respective candidate state (see paragraph [0064], “the transition model provides transition probabilities between pairs of states for each of a number of available recommended actions”) is determined based on historical behavior data of users indicating a frequency at which the users transitioned from the at least respective candidate state to the target state after transitioning from the current state to the at least respective candidate state (paragraph [0037] and [0038], “The transition model update module 112 can take the passive data and construct, for instance, n-grams to predict the impact of next recommended actions given n-history of actions. The transition model update module 112 is deployed incrementally where at each epoch it learns transition probabilities (e.g., parameterized MDP transition probabilities) by using a passive model from the passive data 120 as a prior and using active data 122 that is captured at each epoch to update the prior”).
It would have been obvious to a person of ordinary skill in the art before the effective
filing date of the claimed invention to modify Woiceshyn with the teachings of Chaturapruek because this allows the learning agent to more quickly and efficiently learn an optimal policy and allows model expressiveness to increase as more active data becomes available (see [0004] of Chaturapruek).
Regarding Claim 2, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches selecting the target state for the user comprises selecting a particular candidate state for which a probability of the user transitioning from the current state to the particular state is less than a threshold (paragraph [0038] of Chaturapruek, “A linking function provides a bridge between the passive data and the transition probabilities. In other words, the linking function provides for the difference between p
(s' l s) provided by the passive data and p (s' l s, a) required for transition probabilities”).
Regarding Claim 3, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches selecting the target state for the user comprises selecting a state that is absent from the set of candidate states (paragraph [0044] of Chaturapruek, “The updated recommendation policy is provided to search engine 123 to perform another iteration of the bootstrapped training of learning agent 104. That is, the updated recommendation policy is employed provide additional recommendations to the user community”).
Regarding Claim 4, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches determining the probability at which the user will transition from the current state to the target state through at least the candidate state comprises determining a probability at which the user will transition from the current state to the target state through a sequence of candidate states including the candidate state (paragraph [0037] and [0038] of Chaturapruek, “The transition model update module 112 can take the passive data and construct, for instance, n-grams to predict the impact of next recommended actions given n-history of actions. The transition model update module 112 is deployed incrementally where at each epoch it learns transition probabilities (e.g., parameterized MDP transition probabilities) by using a passive model from the passive data 120 as a prior and using active data 122 that is captured at each epoch to update the prior”).
Regarding Claim 5, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches determining, based on updated data received from the plurality of sensors, that the user has transitioned from the current state to the next state (see paragraph [0075] of Woiceshyn for using sensor data to determine user state and paragraph [0065] of Chaturapruek for repeating the user action recommendation after the user transitions to the next state, “The process of: updating the transition model from available active data, providing a recommended action, and updating the active data from blocks 504-512 is repeated for each epoch of interaction between a user device and the recommendation system”);
updating the probability for each candidate state based at least on the next state (see paragraph [0064] of Chaturapruek, “As shown at block 506, a transition model of the recommendation system is updated using the passive data and the currently available active data. As previously discussed, the transition model provides transition probabilities between pairs of states for each of a number of available recommended actions”);
selecting an additional next state based on the updated probabilities (see paragraph [0065] of Chaturapruek, “The currently available active data is also updated based on the recommended action and the previous state and new state, as shown in block 512”);
initiating one or more additional actions to transition the user from the next state to the additional next state (paragraph [0032] of Woiceshyn, “Notably, the server computing device 206 can communicate the action suggestion 128 from the network system 204 to the computing device 202 via the network 114, as indicated at 216, where the action suggestion 128 can be displayed for the user, such as in a device user interface”).
Regarding Claim 6, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches after initiating the one or more actions, determining, based on updated data received from the plurality of sensors, that the user is performing actions to prevent the transition to the next state (see paragraph [0022] of Chaturapruek, “A reward signal (or simply a reward) is provided to the learning agent for each epoch that can be based on the recommended action (i.e., a recommendation associated with the content) and a new state resulting from a user action taken in response to the recommended action. The reward signal may be positive or negative”; Examiner interprets a negative reward signal as an indication that the user is performing actions that prevent transition to the next state);
in response to determining that the user is performing actions to prevent the transition to the next state, stopping the one or more actions or performing one or more additional actions to maintain the user in the current state (see paragraph [0033] of Chaturapruek, “At a high level, the sequential recommendation system 104 includes a learning agent 108 that is trained to iteratively provide recommended actions to the user device 102 over epochs. For each epoch: the learning agent 108 provides a recommended action to the user device 102 based on a current state; information is returned regarding a user action taken after providing the recommended action; a new state is derived based at least in part on the recommended action and user action; and a reward is provided for training the learning agent 108. The learning agent 108 uses such information to improve its recommendation algorithm at each epoch”).
Regarding Claim 7, the rejection of Claim 1 is incorporated herein. Woiceshyn as modified by Chaturapruek teaches determining to transition the user to the target state based at least on the data received from the plurality of sensors (paragraph [0028] of Woiceshyn, “In response to identifying the particular user state parameter(s) and/or particular environment context parameter(s) with the higher probability and/or correlation to causing the change in mood, the action generation module 104 generates an action suggestion 128 that is intended to modify the current user state 116”).
Claims 8-14 recite a system which performs substantially similar steps as listed by the method in Claims 1-7, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Woiceshyn teaches a computer-implemented system, comprising: one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform operations (paragraph [0033], “The server computing device 206 includes memory 218 and a processor 220, and may include any number and combination of different components as shown and described with reference lo the example device shown in FIG. 11).” Examiner interprets one or more computers as generic device(s) to accomplish the program, the described data processing method. The broadest reasonable interpretation of a computer is one with computer readable memory storing the method in order to perform the operations.)
Claims 15-21 recite a non-transitory medium which stores substantially similar as listed by the method in Claims 1-7, respectively, and are rejected with the same rationale, mutatis mutandis. Additionally, Woiceshyn teaches a non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations (paragraph [0033], “The server computing device 206 includes memory 218 and a processor 220, and may include any number and combination of different components as shown and described with reference lo the example device shown in FIG. 11).” Examiner interprets one or more computers as generic device(s) to accomplish the program, the described data processing method. The broadest reasonable interpretation of a computer is one with computer readable memory storing the method in order to perform the operations.
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 LI B ZHEN whose telephone number is (571)272-3768. The examiner can normally be reached M-F, 7:30a-4p.
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/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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