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 . This action is in response to the application filed on February 27, 2024. Claims 1-20 are pending in the application and have been examined.
Claim Interpretation
Claim 12 recites a computer program product comprising one or more computer readable storage media, para. [0021] of the specification explicitly defines computer readable storage medium to exclude signals per se.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to step 1 of the subject matter eligibility guidance, claim 1 recites a method of facilitating processing within a computing environment, thus a process, one of the four statutory categories of patentable subject matter. However, in reference to step 2A prong 1, the claim further recites the steps generating … prompts for a user of a computing system to facilitate customized assistance to the user in carrying out a computing-based process, the generating comprising: identifying … the computing-based process initiated by the user via the computing system where merely evaluating the user’s behavior, recites an abstract idea, being a mental process; determining, … one or more typical actions of the user where merely judging an action to be typical or not recites a mental process; producing, … based on the one or more typical actions of the user relevant to the computing-based process, … prompts for the user where merely determining text for a prompt recites a mental process. Thus, the claim recites an abstract idea of identifying a user process based on the user’s behavior and determining a prompt for assisting the user based on typical user actions.
In reference to step 2A prong 2, the claim does not include any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
a machine learning model to perform the mental processes (e.g. “providing, by the artificial intelligence agent”, “using a machine learning model”) which by MPEP 2106.05(f)(2), consists of no more than “using a computer of other machinery as a tool” to perform the mental process, i.e. “apply it” which cannot integrate the abstract idea into a practical application; and
providing … the one or more electronic prompts to the user’s computing system, which is an insignificant extra-solution activity that does not integrate the abstract idea into a practical application (see MPEP 2106.05(g)); and
identifying the process by using user data including historical user data, which is merely specifying data used in the mental process (see MPEP 2106.05(h)) which does not integrate the abstract idea into a practical application; and
relevant to the computing-based process merely restricts the abstract idea to a particular field of use, by MPEP 2106.05(h).
Thus, the claim is directed towards the abstract idea of identifying a user process based on the user’s behavior and determining a prompt for assisting the user based on typical user actions.
In reference to step 2B, the additional elements cannot, either alone or in combination, provide an inventive concept nor significantly more than the abstract idea itself, because (a) by MPEP 2106.05(f)(2), merely implementing the abstract idea on generic computer components or other machinery cannot provide an inventive concept, because (b) outputting data from memory is well-understood, routine, and conventional by MPEP 2106.05(d), because (c,d) merely specifying the data used or the particular field of use of the abstract idea does not provide significantly more than the abstract idea itself (see MPEP 2106.05(h), and because there is no nexus between the additional elements which, in combination, can provide significantly more than the abstract idea itself.
In reference to step 1 of the subject matter eligibility guidance, claim 2 is dependent on the computer-implemented method of claim 1, thus a process, one of the four statutory categories of patentable subject matter. However, by step 2A prong 1, claim 2 further recites retraining the machine learning model which merely recites a mental process of learning.
By step 2A prong 2, the additional element of claim 2 recites using, at least in part, reinforcement machine learning to perform the mental process. The additional element is merely “using a computer of other machinery as a tool” to perform the mental process, i.e. “apply it”, which cannot integrate the abstract idea into a practical application (see MPEP 2106.05(f)(2)).
In reference to step 2B, by MPEP 2106.05(f)(2), merely implementing the abstract idea on generic computer components or other machinery cannot provide an inventive concept or provide significantly more than the abstract idea itself.
Claim 3 is dependent on the computer-implemented method of claim 2, thus is also directed to an abstract idea, without significantly more. Claim 3 only limits the additional element of reinforcement machine learning with gamification which remains a mental process of learning to perform the mental process. The additional element is “using a computer or other machinery merely as a tool” to perform the mental process, i.e. “apply it”, which cannot integrate the abstract idea into a practical application (see MPEP 2106.05(f)(2)) nor provide significantly more than the abstract idea itself.
Claim 4, dependent on claim 1, only recites an additional mental process step (i.e. the machine learning model comprises a K-nearest neighbor (KNN) algorithm, where assigning weights to the nearest data points based on a specific value, and determining a label of that grouping is something that can be done in the mind, with assistance of pencil and paper) but no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 5, dependent on claim 1, merely recites present, at least in part, the user with a user-specific path which is insignificant extra-solution activity of merely providing the output of the mental process, which by MPEP 2106.05(g) cannot integrate the abstract idea into a practical application. The additional element cannot provide significantly more than the abstract idea itself because storing and displaying data are well-understood, routine, and conventional (MPEP 2106.05(d), “storing and retrieving data from memory”, “receiving or transmitting data over a network”). Therefor the claim is subject-matter ineligible.
Claim 6, dependent upon claim 1, only specifies a limitation to the field of use of the data for which the mental process is based (i.e. comprises user data from other computing-based processes) which, by MPEP 2106.05(h), “limiting an abstract idea to one field of use … did not make the concept patentable”, and thus cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 7, dependent on the method of claim 1, only provides further definition of the abstract idea step of identifying … the … process, i.e. by the mental process of ascertaining a data-analysis-based intention. Thus, the claim recites an abstract idea of identifying a user’s intended process based on the user’s behavior. Further, the claim does not recite any additional element which could integrate this abstract idea into a practical application, because the additional element consists of collecting … the user data, that is, insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)). The additional element cannot provide significantly more than the abstract idea itself because collecting data is well-understood, routine, and conventional (MPEP 2106.05(d), “transmitting or receiving data over a network”), thus the claim is ineligible.
Claim 8, dependent on claim 1, merely specifies a limitation to the field of use of the data for which the mental process is based (i.e. typical actions of the user … include one or more historical user behavioral actions, including any user pauses) which, by MPEP 2106.05(h), “a particular type of data … could be considered … a field of use” and cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 9, dependent on claim 1, recites an additional mental process step of identifying [that] … one or more components … can be automated, but no new additional elements, thus no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim 10, dependent on claim 1, merely recites providing … one or more a user interface overlays which is insignificant extra-solution activity of merely providing the output of the mental process, which by MPEP 2106.05(g) cannot integrate the abstract idea into a practical application. The additional element cannot provide significantly more than the abstract idea itself because storing and displaying data are well-understood, routine, and conventional (MPEP 2106.05(d), “storing and retrieving data from memory”, “receiving or transmitting data over a network”). Thus, the claim is subject-matter ineligible.
Claim 11, dependent on claim 1, merely specifies the field of use the user data is based on for generating the output of the mental process (i.e. generated … for the one user of the multiple users) which, by MPEP 2106.05(h), “limited to a particular data source … or a particular type of data … could be considered to be … a field of use” and cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim(s) 12-16 recites a product to perform the method of claims 1,2,5,7,9, respectively. The product recites the additional element of a set of one or more computer readable storage media, and thus claims 12-16 are rejected for reasons set forth in the rejections of Claims 1, 2, 5, 7, and 9, respectively, and because performing a mental process on generic computer components is considered merely using a computer or “other machinery” as a tool to perform the abstract idea, i.e. “apply it”, by MPEP 2106.05(f) and does not integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim(s) 17-20 recites a system to perform the method of claims 1-4, respectively. The product recites the additional element of at least one processor set, and thus claims 17-20 are rejected for reasons set forth in the rejections of Claims 1-4, respectively, and because performing a mental process on generic computer components is considered merely using a computer or “other machinery” as a tool to perform the abstract idea, i.e. “apply it”, by MPEP 2106.05(f) and does not integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 5-10, 12, and 14-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dotan-Cohen et al., US PG Pub 2021/0374579 A1.
Regarding Claim 1, Dotan-Cohen teaches a computer-implemented method of facilitating processing within a computing environment, the computer-implemented method comprising: generating, by an artificial intelligence agent (Dotan-Cohen et al., pg. 10, para. [0070], “pattern inferences logic 230 may employ machine-learning mechanisms”, where the “machine-learning mechanisms” denotes artificial intelligence and the “pattern inferences logic” denotes an agent), one or more electronic prompts for a user of a computing system to facilitate customized assistance to the user in carrying out a computing-based process (Dotan-Cohen et al., pg. 19, para. [0134], “enhanced user experience comprises one of a recommendation, notification, request, or suggestion related to the probable future activity”, where the “recommendation, notification, request, or suggestion” denotes the prompts for the user and “enhanced user experience … related to the probable future activity” denotes for “customized assistance to the user in carrying out a computing-based process”), the generating comprising: identifying, by the artificial intelligence agent with reference to user data, the computing-based process initiated by the user via the computing system (Dotan-Cohen et al., pg. 2, para. [0018], “the user activity pattern may be analyzed along with sensor data collected by a user device, and the user's intent inferred”, where the user activity pattern to be analyzed denotes the user data and the user’s intent inferred denotes identifying the process initiated), the user data including historical user data relevant to the computing-based process (Dotan-Cohen et al., pg. 10, para. [0067], “Behavior features may comprise behaviors such as user activities that tend to occur with certain locations or activities occurring before or after a given user activity event (or sequence of previous activity events)”, where “user activities” denotes user data and “previous activity events” denotes historical user data); determining, by the artificial intelligence agent using a machine learning model and the user data, one or more typical actions of the user relevant to the computing-based process (Dotan-Cohen et al., pg. 19, para. [0134], “the enhanced user experience comprises one of a recommendation, notification, request, or suggestion related to the probable future activity”, where the pattern inference logic utilizes machine-learning with input from user data to create user relevant probable future activities); producing, by the artificial intelligence agent based on the one or more typical actions of the user relevant to the computing-based process, the one or more electronic prompts for the user (Dotan-Cohen et al., pg. 19, para. [0134], “the enhanced user experience comprises one of a recommendation, notification, request, or suggestion related to the probable future activity”, where “providing a recommendation or suggestion” denotes producing the one or more prompt and “related to the probably future activity” denotes the typical actions of the user); and providing, by the artificial intelligence agent, the one or more electronic prompts to the user’s computing system to provide the customized assistance to the user in carrying out the computing-based process (Dotan-Cohen et al., pg. 1, para. [0009], “provide personalized computing experiences and other services tailored to the user, such as timely, relevant delivery or presentation of content, or modifying content that would otherwise be presented; … incorporation of the user's routine into recommendations and notifications”, where providing personalized computing presentations denotes prompt outputs to the user and personalized recommendations denote customized assistance).
Regarding Claim 5, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the one or more electronic prompts of the artificial intelligence agent present, at least in part, the user with a user-specific path forwarded in carrying out the computing-based process (Dotan-Cohen et al., pg. 19, para. [0134], “enhanced user experience comprises one of a recommendation, notification, request, or suggestion related to the probable future activity”, where providing a “enhanced user experience … of a recommendation, notification, request, or suggestion” denotes prompt outputs to the user and “related to the probable future activity” denotes a user specific path forwarded to carry out the computing-based process as “a future activity event, … may include one or a series (or sequence) of future user interactions likely to occur or likely to be desired by the user to transpire.”(Dotan-Cohen et al., pg. 19, para. [0130])), based on the one or more typical actions of the user relevant to the computing-based process (Dotan-Cohen et al., pg. 19, para. [0134], “enhanced user experience comprises one of a recommendation, notification, request, or suggestion related to the probable future activity”, where a recommendation “related to the probably future activity” denotes based on the one or more typical actions of the user).
Regarding Claim 6, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the user data further comprises user data from other computing-based processes relevant to the computing-based process (Dotan-Cohen et al., pg. 10, para. [0069], “activity pattern determiner 266 (or activity pattern inference engine 260) may determine a user activity pattern based on repetitions of similar activity features associated with a plurality of observed activity events.”).
Regarding Claim 7, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein identifying, by the artificial intelligence agent, the computing-based process comprises collecting, by the artificial intelligence agent, the user data (Dotan-Cohen et al., pg. 10, para. [0069], “activity pattern determiner 266 (or activity pattern inference engine 260) may determine a user activity pattern based on repetitions of similar activity features associated with a plurality of observed activity events.”, where plurality of observed activity events denotes collecting user data) and ascertaining a data-analysis-based intention of the user and using, by the artificial intelligence agent, the data-analysis-based intention in identifying the computing-based process (Dotan-Cohen et. al, pg. 1, para. [0009], “The user activity patterns may be used to infer user intent or predict future activity of the user. From these predictions or inferred user intent, various implementations may provide personalized computing experiences and other services tailored to the user, such as timely, relevant delivery or presentation of content, or modifying content that would otherwise be presented; improvements to user device performance and network bandwidth usage; incorporation of the user's routine into recommendations and notifications”, where “to infer user intent or predict future activity of the user” denotes identifying the intended process through a data-analysis and user activity patterns used to infer denote collecting user data).
Regarding Claim 8, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the one or more typical actions of the user relevant to the computing-based process include one or more historical user behavioral actions, including any user pauses (Doten-Cohen et al., pg. 2, para. [0022], “collecting user data with one or more sensors or components on user device(s) associated with a user. … user data … may include information about the user device(s), user activity associated with the user devices (e.g., app usage, online activity, searches, calls, usage duration, and other user-interaction data)”), related to the user’s carrying out of the computing-based process (Doten-Cohen et al., pg. 19, para. [0131], “the probable future activity event is determined looking at a periodic or behavior context, which may be defined according to periodic features or behavioral features … a periodic context defines when an activity event associated with an activity pattern is likely to occur. … Similarly, a behavioral context defines contextual features present when an activity pattern is likely to occur, such as where the pattern indicates a user performs a particular activity”).
Regarding Claim 9, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein generating the one or more electronic prompts further comprises identifying, by the artificial intelligence agent, one or more components of the computing-based process can be automated by the artificial intelligence agent based, at least in part, on the one or more typical actions of the user relevant to the computing-based process, and the producing of the one or more electronic prompts further being based, at least in part, on the artificial intelligence agent determining that the one or more components of the computing-based process can be automated (Dotan-Cohen et al., pg. 14, para. [0100], “In some embodiments, … based on a prediction or inference determined from the imputed pattern (and in some embodiments based further on a confidence score associated with the prediction or inference), the volume may be automatically attenuated (or amplified), or the user may be prompted about whether the user desires a service to automatically handle adjusting the volume”).
Regarding Claim 10, Dotan-Cohen teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein providing the one or more electronic prompts further comprises providing, by the artificial intelligence agent, the one or more electronic prompts to a user interface of the computing system for display to the user as one or more user interface overlays during the computing-based process (Dotan-Cohen et al., pg. 15, para. [0104], “In some embodiments, presentation component 220 generates user interface features associated with the personalized content. Such features can include interface elements (such as graphics buttons, sliders, menus, audio prompts, … in-app notifications, or other similar features for interfacing with a user), queries, and prompts”).
Regarding Claim 11, Dotan-Cohen et al. teaches the computer-implemented method of claim 1 (and thus the rejection of Claim 1 is incorporated), wherein the user is one user of multiple users of the computing-based process, and the one or more electronic prompts are generated by the artificial intelligence agent specifically for the one user of the multiple users of the computing-based process. (Dotan-Cohen et al., pg. 18, para. [0128], “user-data collection component 210 (FIG. 2) can accumulate user data and interpretive data for multiple users or user devices, such that each user device does not require separate and redundant data collection and storage”)
Claims 12, 14-16 recite a computer program product to perform precisely the method of claims 1, 5, 7, and 9 respectively. As Dotan-Cohen et al. teaches the method being applied to a product with instructions on a memory and executable by a processor due to their disclosure, “The methods may also be embodied as computer-usable instructions stored on computer storage media” (Dotan-Cohen et al., pg. 16, para. [0113]). Claims 12, 14-16 are rejected for reasons set forth in the rejections of claims 1,5,7, and 9 respectively.
Claim 17 recites a computer system to perform precisely the method of claim 1. As Dotan-Cohen et al. teaches the method being applied to a system with processors and instructions on memory due to their disclosure, “The methods may also be embodied as computer-usable instructions stored on computer storage media” (Dotan-Cohen et al., pg. 16, para. [0113]) and “computing device 600 includes a bus 610 that directly or indirectly couples the following devices: memory 612, one or more processors” (Dotan-Cohen et al., pg. 20, para. [0137]). Claim 17 is rejected for reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, 4, 13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Dotan-Cohen et al., US PG Pub 2021/0374579 A1, in view of Williams et al., US PG Pub 2024/0330654 A1.
Regarding Claim 2, Dotan-Cohen et al. teaches the computer-implemented method of claim 1 (thus the rejection of Claim 1 is incorporated).
Dotan-Cohen does not teach, but Williams et al. teaches further comprising retraining the machine learning model using, at least in part, reinforcement machine learning (Williams et al., pg. 4, para. [0046], “The voice bot or chatbot 150 may employ supervised or unsupervised machine learning techniques, which may be followed or used in conjunction with reinforced or reinforcement learning techniques.”) based on further user data derived from one or more further user actions during the computing-based process (Williams et al., pg. 18, para. [0181], clm. 5, “receive an authorization from the user to track a progress of the user in completing the one or more discrete steps, monitor the progress of the user, and communicate the progress to the user.”), the retraining facilitating effectiveness of generated electronic prompts over time (Williams et al., pg. 6, para. [0063], “The reward model 220 may be required to leverage Reinforcement Learning with Human Feedback (RLHF) in which a model (e.g., ML chatbot model 250) learns to produce outputs which maximize its reward 225, and in doing so may provide responses which may be better aligned to user prompts.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement retraining using reinforcement learning applied in the personalized planning system of Williams et al. similarly into the enhanced user assistance system of Dotan-Cohen. The motivation to do so being to “receive a reward signal based upon the reward signal definition and the ML output, and alter the decision-making model so as to receive a stronger reward signal for subsequently generated ML outputs.” (Williams et al., pg. 4, para. [0039]), where stronger reward signals denote more accurately generated user assistance experiences.
Regarding Claim 4, Dotan-Cohen et al. teaches the computer-implemented method of claim 1 (thus the rejection of Claim 1 is incorporated) but does not teach wherein the machine learning model comprises a K-nearest neighbor (KNN) algorithm (Williams et al., pg. 8, para. [0080], “The ML model 310 may be trained to generate the plan instructions 360 via a … k-nearest neighbor algorithm”). It would have been obvious to one of ordinary skill in the art before the effective file date of the claimed invention to implement KNN into the method. The motivation to do so being “to train the ML model 310 to generate the plan instructions” (Williams, pg. 8, para. [0080]), which can be completed by the KNN algorithm for the similar application of training for a “computer-implemented method for personalized planning using ML” (Williams et al., pg. 1, para. [0007]).
Claim 13 recites a computer program product to perform precisely the method of claim 2. As Dotan-Cohen et. al teaches the method being applied to a product with instructions on a memory and executable by a processor due to their disclosure, “The methods may also be embodied as computer-usable instructions stored on computer storage media” (Dotan-Cohen et al., pg. 16, para. [0113]), claim 13 is rejected for reasons set forth in the rejection of claim 2.
Claims 18 and 20 recite a computer system to perform precisely the method of claims 2 and 4 respectively. As Dotan-Cohen et al. teaches the method being applied to a system with instructions on a memory and processors due to their disclosure, “The methods may also be embodied as computer-usable instructions stored on computer storage media” (Dotan-Cohen et al., pg. 16, para. [0113]) and “computing device 600 includes a bus 610 that directly or indirectly couples the following devices: memory 612, one or more processors” (Dotan-Cohen et al., pg. 20, para. [0137]), claims 18 and 20 are rejected for reasons set forth in the rejections of claims 2 and 4 respectively.
Claim(s) 3 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dotan-Cohen et al., US PG Pub 2021/0374579A1, in view of Williams et al., US PG Pub 2024/0330654 A1, and further in view of Singh et al., US PG Pub 2024/0311684 A1.
Regarding Claim 3, the Dotan-Cohen/Williams combination for the rejection of Claim 2 teaches the computer-implemented method of claim 2 (thus the rejection of Claim 2 is incorporated).
Dotan-Cohen/Williams combination does not teach, but Singh et al. teaches the gamification to enhance effectiveness of the generated electronic prompts over time (Singh et al., pg. 3, para. [0044], “Gamification is used to train an artificial intelligence to make recommendations.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement gamification into the computer-implemented method. The motivation to do so being improved training accuracy and user data for reinforcement learning for improved efficiency and user experience (Singh et al., pg. 7, para. [0084], “Due to previous gamification in training, the policy 1112 outputs recommendations more likely to improve KPI, such as the interface”, where KPI refers to key performance measures such as customer satisfaction, employee satisfaction, productivity, etc.).
Claim 19 recites a computer system to perform precisely the method of claim 3. As Dotan-Cohen et al. teaches the method being applied to a system with instructions on a memory and processors due to their disclosure, “The methods may also be embodied as computer-usable instructions stored on computer storage media” (Dotan-Cohen et al., pg. 16, para. [0113]) and “computing device 600 includes a bus 610 that directly or indirectly couples the following devices: memory 612, one or more processors” (Dotan-Cohen et al., pg. 20, para. [0137]), claim 19 is rejected for reasons set forth in the rejection of claim 3.
Conclusion
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/H.Y.A./Examiner, Art Unit 2122 /BRIAN M SMITH/Primary Examiner, Art Unit 2122
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