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 .
DETAILED ACTION
Claims 1-10 are pending. Claims 1 and 9-10 are independent.
This Application was published as U.S. 20250200304.
Apparent priority: 19 December 2023.
This Application pertains to prompting a machine learning model to use a predictive model pertaining to a special factor:
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It may further prosecution if the independent Claims specify that the special factors are event of a predetermined duration and event type and to define the predictive models with particularity so that a generic model cannot be mapped to the claim. The terminology of the Claim is currently quite broad and not representative of potential intent.
Suggestion: add dependent Claims listing examples of key terms of the Claims. For example: 11. The method of claim 1, wherein the special factor is selected from the following: a sports event, a disease epidemic event, a government sponsored event, ….
See the following from the published Application:
[0036] FIG. 1 illustrates an overall configuration of a management system (hereinafter, simply referred to as a “management system”) of a machine learning model to which an information processing device according to the present disclosure is applied. A manager of a machine learning model or the like (hereinafter, simply referred to as a “user”) requests information concerning a special factor which may affect prediction to a management system 1 in a case where a decrease in prediction accuracy (hereinafter, also referred to as a “prediction error”) occurs during an operation of a prediction process using the machine learning model. The management system 1 provides information concerning the special factor if there is any special factor which may affect the prediction. Moreover, in a case where there is a predictive model usable under the special factor, the management system 1 proposes that predictive model. Accordingly, it is possible for the user to respond to the prediction error by, for instance, changing the machine learning model used for the prediction when the prediction error occurs due to the special factor.
[0040] The prediction task designated by the user includes, for instance, various prediction tasks such as prediction of product demand, prediction of electric power demand, and prediction of weather. Note that in the following description, the prediction task designated by the user is assumed to be a prediction of product demand in a certain store.
[0041] The terminal device 20 acquires the information concerning the predictive model which can be used under special factor and presents the information to the user. Thus, it is possible for the user to obtain information concerning the special factor which is considered to be a cause of the prediction error and the predictive model which can be used under the special factor by performing the input by natural language.
Appears that certain predictive models work better with certain “special factors” and the system of the invention looks at the “special factor” and suggests the best “predictive model.”
The “special factors” are shown in Figure 7 and are just the type of query/question such as information regarding “a sports event” or an “epidemic of a disease.” This seems like a domain-specific model.
The “explanatory variables” are shown in Figure 6 and examples include: “Date/Time,” “Day of week, “Weather, “number of visitors.” This seems like context or condition or parameters leading to a particular result.
The “predictive models” are shown in Figure 5 and are a weighted sum of the “explanatory variables.”
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
While the subject matter of the Application is primarily technological the Claims are broad and would benefit from definition of the terms that would convey the invention. The Claim is looking for a model that is best suited to a prediction task and because a person conducts prediction tasks as a matter of course based on his previous experience (models) the Claim needs to include language that provides the machine related steps with more technological particularity. First: define what the terms mean inside the Claim and Second: establish a technological relationship between the limitations.
Step 1: The independent Claims are directed to statutory categories:
Claim 1 is a system claim and directed to the machine or manufacture category of patentable subject matter.
Claim 9 is a method claim and directed to the process category of patentable subject matter.
Claim 10 is a computer-readable-storage device claim and is directed to the machine or manufacture category of patentable subject matter.
Step 2A, Prong One: Does the Claim recite a Judicially Recognized Exception? Abstract Idea? Are these Claims nevertheless considered Abstract as a Mathematical Concept (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion), or Certain Methods of Organizing Human Activity (1-fundamental economic principles or practices (including hedging, insurance, mitigating risk), 2-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), 3- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and fall under the judicial exception to patentable subject matter?)
The rejected Claims recite Mental Processes or Methods of Organizing Human Activity.
Step 2A, Prong Two: Additional Elements that Integrate the Judicial Exception into a Practical Application? Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
The rejected Claims do not include additional limitations that point to integration of the abstract idea into a practical application and are therefore directed to the abstract idea.
Claim 1 is a generic automation of a mental process of responding to a question regarding an event based on past experiences (models) of the person or by accessing a particular app (model) on his/her phone. Whether this Claim is mapped to a person’s internal mental thoughts or a very high-level generic interaction with a computer the Claim includes an abstract idea at a very high level.
[Claim 1] An information processing device comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
acquire a prompt described in natural language which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task; [Jack asks Jill (prompt entered by Jack) how long she thinks the baseball game will last. Alternatively, Jack can enter a prompt into an LLM like ChatGPT.]
interpret the prompt using natural language, and acquire information concerning the special factor which may affect the prediction task designated; [Jill receives the question (prompt) in natural language and checks the weather. (ChatGPT can do the same thing.)]
acquire model information concerning a model which can be used in a case of corresponding to the special factor; and [Jill is accessing a meteorological model when she checks the weather.]
output an answer including information concerning the special factor and the model information acquired. [Jill responds to Jack that because it will be raining the baseball game may be canceled.]
Step 2B: Search for Inventive Concept: Additional Elements Do not amount to Significantly More: The limitations of processors and memory are well-understood, routine, and conventional (werc) machine components that are being used for their well-understood, routine, conventional and rather generic functions. Additionally, these limitations are expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they are not sufficient to cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
The Dependent Claims do not add limitations that could integrate the abstract idea into a practical technological application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim:
[Claim 2] The information processing device according to claim 1, wherein the processor acquires the model information from a storage unit which stores, for each special factor, special factor data indicating a relationship between the special factor and the model which can be used in the case of corresponding to the special factor. [Jill knows where to access the weather app on her phone to get the information she needs.]
[Claim 3] The information processing device according to claim 2, wherein
the special factor data are described in natural language; and [Jill knows that if it rains the game will be cancelled and this is provided on the baseball team website.]
the processor interprets information concerning the special factor, and acquires the model information by referring to the special factor data. [Jill interprets the information “no game if rain” and accesses the weather app.]
[Claim 4] The information processing device according to claim 3, wherein
the special factor data include a type of the special factor; and [The baseball game includes the type of the game as baseball.]
the processor acquires the model information based on the type of the special factor. [Jill checks the weather (model) because she knows that baseball games are affected by the weather.]
[Claim 5] The information processing device according to claim 4, wherein
the prompt includes a request of information concerning a type of the special factor; and [The question (prompt) from Jack is regarding a baseball game (type).]
the answer includes the type of the special factor. [The answer will include the name “baseball” / type.]
[Claim 6] The information processing device according to claim 3, wherein
the special factor data include duration of the special factor; and [The duration of the game is included.]
the processor acquires the model information based on the duration of the special factor. [The duration of the game impacts which model (weather or traffic) is used to come up with an answer.]
[Claim 7] The information processing device according to claim 6, wherein
the prompt includes a request of information concerning the duration of the special factor; and [The question may ask how long the game will be.]
the answer includes the duration of the special factor. [Jill can answer 4 hours.]
[Claim 8] The information processing device according to claim 1, wherein the processor outputs a message indicating that no model is available if no model is available to use in the case of corresponding to the special factor. [If Jack asks do you think the game will be cancelled because the pitcher will get injured. Jill can answer I cannot answer that question Jack; I have no way of figuring it out.]
With respect to Independent Claim 9 and independent Claim 10, which have limitations similar to the limitations of Claim 1, there are no additional limitations (method Claim) or the additional limitation (non-transitory computer-readable medium) falls under well-understood, routine, and conventional (werc) machine components that are being used for their well-understood, routine, conventional and is not expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they do not include additional limitations that can 1) integrate the Abstract Idea into a practical application or 2) cause the Claim as a whole to amount to more than the underlying abstract idea.
[Claim 9] An information processing method performed by a computer, comprising:
acquiring a prompt described in natural language which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task;
interpreting the prompt using natural language, and acquiring information concerning the special factor which may affect the prediction task designated;
acquiring model information concerning a model which can be used in a case of corresponding to the special factor; and
outputting an answer including information concerning the special factor and the model information acquired.
[Claim 10] A non-transitory computer-readable recording medium storing a program causing a computer to execute processing of:
acquiring a prompt described in natural language which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task;
interpreting the prompt using natural language, and acquiring information concerning the special factor which may affect the prediction task designated;
acquiring model information concerning a model which can be used in a case of corresponding to the special factor; and
outputting an answer including information concerning the special factor and the model information acquired.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 9-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Klein (U.S. 20220308718).
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Regarding Claim 1, Klein teaches:
1. An information processing device comprising:
at least one memory configured to store instructions; and [Klein, Figure 19, “Memory 12.”]
at least one processor configured to execute the instructions to: [Klein, Figure 19, “Processors 14.”]
acquire a prompt described in natural language [Klein, Figure 3, “Client 303.” “Utterance Data …305” is part of the input.]
which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task; [Klein, Figure 3, the “utterance data” includes the designation of a task and because this Claim provides no definition of the “special factor” it is interpreted as the “user view context” of Klein or more generally the context for a command. Prediction task is mapped to user intent prediction of Klein which is based on context/special factor. “[0086] Responsively, the intermediary service 307 passes both the speech-to-text data 311 along with the user view context (included in the request 305) in a request 315 to the user intent understanding service 317 in order to determine or predict a user intent of the voice utterance associated with the speech-to-text data 311. …” “[0088] FIG. 4A is a schematic diagram illustrating how a user view context is used to initialize a data structure for predicting user intent …” Absent a definition for the “special factor” in the Claim, results that are based on context/special factor are also “concerning the special factor.”]
interpret the prompt using natural language, and acquire information concerning the special factor which may affect the prediction task designated; [Klein, Figure 3, the “information concerning the special factor” is taught by the “user view context” and both the input utterance and context/special factor are interpreted using NLP models. The Interpretation is conducted at “Speech Recognition Service 313” which produces the “speech-to-text data.” “[0086] … As described above, such user intent can be determining based on using one or more NLP models to determine semantic meaning in the textual data, as well as using various sources of context (e.g., user SMS text messages, email threads, graph structures, and the like), which includes the user view context.” Figure 4B, 404: Populate meeting attendees field under “Client Action Request.” “[0094] … The client action request attribute is indicative of a command to the client application to perform one or more specific actions based on determining the user intent. Specifically, the client action request as indicated in the table 404 is to populate the “meeting attendees” field of instance ID 4….” “[0069] In some embodiments, the context understanding module 218 represents or includes one or more language understanding models or services to understand the semantic meaning (or user intent) of a voice utterance. Such understanding can include using NLP-based functionality or models, such as WORD2VEC, BERT, RoBERTa, and/or the like….”]
acquire model information concerning a model which can be used in a case of corresponding to the special factor; and [Klein, Figure 3, “user Intent Understanding Service 317” determines the model/application to be used according to the intent and context. “[0068] The context understanding module 218 is generally responsible for determining or predicting user intent of a voice utterance issued by a user. “User intent” as described herein refers to one or more actions or tasks the user is trying to accomplish via the voice utterance. In some embodiments, a user intent alternatively or additionally refers to the specific user interface task the user is trying to accomplish within a client application….” The Applications/Models are identified from context/special factor and from the input utterance/prompt: “[0072] … For example, if the user view context module 222 obtains information that a user currently has a window of an email application open, where the window has a “to” field populated with “John Doe” and the user has additionally issued a voice utterance that says, “add Jake to the message,” the context understanding module 218 can infer that the user intent is to populate the “to” field with Jake's email based on the information in the current user view.” In Figure 6A, the Application/Model is a Chat application. In Figure 7A a Calendar Application/Model is shown. “[0073] In some embodiments, in response to the context understanding module 218 determining user intent, it transmits, over the network(s) 110, a client action request and result payload to a user device that includes the consumer application 204 so that the consumer application 204 can responsively populate the appropriate fields and/or switch to the appropriate instances in order to execute the voice utterance request. …” “[0098] The network graph 500 specifically shows the relationships between various users and applications, such as client applications….” “[0107] … In other embodiments, screenshots 608 and 602 are from separate applications and demonstrate cross-application (or cross-domain) functionality by allowing a user to utilize a second application (e.g., the application of screenshot 608) to complete a task initiated within a first application (e.g., the application of screenshot 602), as further described herein. For instance, such functionality allows a user to send a message via MICROSOFT TEAMS (i.e., the second application) from the MICROSOFT OUTLOOK calendar (i.e., first application), as described below….”]
output an answer including information concerning the special factor and the model information acquired. [Klein, Figure 3, “Client Action Request + Result Payload 319.” Figure 4B, “Jane Doe.” “[0094] … The “result payload” attribute indicates the specific values that are to be returned to the client application based on the client action request and the determined or predicted user intent. Specifically, the result payload is “Jane Doe.” Accordingly, the table 404 may represent a message or control signal to the client application requesting the client application to populate a meeting attendee's field at instance 4 with the result payload of Jane Doe.”]
Regarding Claim 2, Klein teaches:
2. The information processing device according to claim 1, wherein the processor acquires the model information from a storage unit which stores, for each special factor, special factor data indicating a relationship between the special factor and the model which can be used in the case of corresponding to the special factor. [Klein, Figure 5 shows a network graph 500 that show/stores the relationships between the context/special factor and the applications/models that use them. “[0098] The network graph 500 specifically shows the relationships between various users and applications, such as client applications. It is understood that these nodes are representative only. As such, the computer resources may alternatively or additionally be calendars that users have populated, groups that users belong to, chat sessions that users have engaged in, text messages that users have sent or received, and the like. In some embodiments, the edges represent or illustrate the specific user interaction (e.g., a download, sharing, saving, modifying or any other read/write operation) with specific applications and/or the relationships between users in a business unit, for example.” “[0097] FIG. 5 is a schematic diagram of an example network graph 500, according to some embodiments. In some embodiments, the network graph 500 is the structure used by the by the context understanding module 218 to help determine user intent and/or the entity recognition module 214 to determine entities. For example, in an embodiment network graph 500 comprises a graph database, which may be stored in storage 225 (FIG. 2) comprising a single database location or distributed storage (e.g., stored in the cloud). Alternatively or in addition, other data repositories or data structures may be utilized, such as a user profile of information about a particular user (e.g., name, contact information, manager(s), organization chart, responsibilities or permissions, or similar information about the user), or a database of data for the user (e.g., files of the user, email, meetings, calendar, user-activity history, location data, or similar information about the user, the storage of which may require the user's consent)….”]
Regarding Claim 3, Klein teaches:
3. The information processing device according to claim 2, wherein
the special factor data are described in natural language; and [Klein, Figure 2, “user view context module 222” and Figure 3, “… user view context 305” were mapped to the “special factor data. “[0072] … For example, if the user view context module 222 obtains information that a user currently has a window of an email application open, where the window has a “to” field populated with “John Doe” and the user has additionally issued a voice utterance that says, “add Jake to the message,” the context understanding module 218 can infer that the user intent is to populate the “to” field with Jake's email based on the information in the current user view.” This is context in natural language.]
the processor interprets information concerning the special factor, and acquires the model information by referring to the special factor data. [Klein, Figure 2, “user view context module 222” and [0072[ above. Based on the email application being open and based on the values populating the other fields, the module infers that the user intent is to put Jakes email in the “to” coloumn. “0073] In some embodiments, in response to the context understanding module 218 determining user intent, it transmits, over the network(s) 110, a client action request and result payload to a user device that includes the consumer application 204 so that the consumer application 204 can responsively populate the appropriate fields and/or switch to the appropriate instances in order to execute the voice utterance request….”]
Regarding Claim 4, Klein teaches:
4. The information processing device according to claim 3, wherein
the special factor data include a type of the special factor; and [Klein, Figure 2, “request type determining module 226.”]
the processor acquires the model information based on the type of the special factor. [Klein, “Model Information” in the following example is “email” which is an Application/ Model. “[0078] … For example, a voice assistant may issue a voice utterance that says, “are you sure you want to send this email to Victoria?” The user may speak, “yes” but then click “no” shortly thereafter at a user interface. The request type determining module 226 may determine that the request type needed to answer this question is an “email sending” request. With email sending requests, however, it may be impossible to honor the “no” input if the email has already been sent. Accordingly, the requests type delivery module 226 may query a set of rules (e.g., in storage 225) that direct which input to respond to….”]
Claim 9 is a method claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale.
9. An information processing method performed by a computer, comprising:
acquiring a prompt described in natural language which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task;
interpreting the prompt using natural language, and acquiring information concerning the special factor which may affect the prediction task designated;
acquiring model information concerning a model which can be used in a case of corresponding to the special factor; and
outputting an answer including information concerning the special factor and the model information acquired.
Claim 10 is a means plus function system claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale.
10. A non-transitory computer-readable recording medium storing a program causing a computer to execute processing of:
acquiring a prompt described in natural language which includes a designation of a prediction task and a request for information concerning a special factor which may affect the prediction task;
interpreting the prompt using natural language, and acquiring information concerning the special factor which may affect the prediction task designated;
acquiring model information concerning a model which can be used in a case of corresponding to the special factor; and
outputting an answer including information concerning the special factor and the model information acquired.
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.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Klein in view of Borg (U.S. 20130311167).
Regarding Claim 5, Klein does not teach that the user prompt to the machine includes a request regarding the type of context/special factor.
Borg teaches:
5. The information processing device according to claim 4, wherein
the prompt includes a request of information concerning a type of the special factor; and [Borg teaches that type of context/special factor is requested from the user in order to be submitted to the machine as part of the query/prompt: “[0104] In at least some embodiments, a prompt may be displayed requesting a user to input specific types of context information. For example, the display screen 500 may prompt a user to input a reason why an action item is was created (i.e. to input information for the "why" context item). In response to the prompt, a user may specify context information indicating a reason why the action item is was created. In at least some embodiments, a freeform text field may be provided for a user to input the information for the "why" context item….” See also Figure 2 for creation of an action item including context.]
the answer includes the type of the special factor. [Borg, the user specifies the type of context to the machine. “[0103] The example display screen 500 shown in FIG. 5 also includes an interface element 510 for receiving input specifying other context information. For example, the display screen 500 may allow a user to input information associated with a "why" context item, a "where" context item and/or a "who" context item.”]
Klein and Borg pertain to query/command directed for performance of a task to a machine and the use of context as part of the information associated with query/command. It would have been obvious to combine the context type as part of the input to the machine from Borg with the prompt of Klein to provide more information for the conducting of the requested task/action. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Regarding Claim 6, Klein teaches:
6. The information processing device according to claim 3, wherein
the special factor data include duration of the special factor; and [Klein: “special factor” was mapped to the context and some types of context/special factor include a duration condition: “[0058] … In some embodiments, the one or more instances are additionally or alternatively those instances that have been interacted with by a user within a threshold period of time (e.g., 5 minutes) or within a user session, such as a duration of relatively continuous user-activity or interaction with the user device. For example, a user view context may include information from a first page of a client application that was rendered to a user device even though it is not currently being displayed because, for instance, the first page has been closed inside of 2 minutes (e.g., the threshold at which data from a page is no longer considered user view context). ….”]
the processor acquires the model information based on the duration of the special factor. [Klein: see [0058] above. Teaches that a context/special factor is considered relevant only if a duration condition (more than 2 mins) is met.]
Regarding Claim 7, Klein does not teach including the duration of the special factor/context as part of the prompt that is input to the machine.
Borg teaches:
7.The information processing device according to claim 6, wherein
the prompt includes a request of information concerning the duration of the special factor; and [Borg as part of the command/prompt for action includes “timing information” which teaches the “duration of the special factor” of this Claim: “[0101] In some embodiments, more precise timing information may be input by a user. For example, in some embodiments, the interface element 508 may allow a user to specify a precise time period (such as, for example, "one day", "two days", "one week", "one month", etc.). In some embodiments, the interface element 508 may allow a user to specify a calendar date which may be specified in terms of a day, month and/or year.”]
the answer includes the duration of the special factor. [Borg: the answer by the user becomes a part of the prompt to the machine.]
Klein and Borg pertain to query/command directed for performance of a task to a machine and the use of context as part of the information associated with query/command. It would have been obvious to combine the duration of context as part of the input to the machine from Borg with the prompt of Klein to provide more information for the conducting of the requested task/action. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Klein in view of Subramanian (U.S. 9354776).
Regarding Claim 8, Klein does not provide an indication of unavailability.
Subramanian teaches:
8. The information processing device according to claim 1, wherein the processor outputs a message indicating that no model is available if no model is available to use in the case of corresponding to the special factor. [Subramanian: depending on context/ special factor, the availability of a service/model is indicated on the screen. “In-context compatibility of one or more services or application instances running as services with currently active work is monitored. Respective context-sensitive icons indicate contextual compatibility and availability of corresponding services. The context-sensitive icon is displayed in a graphical user interface of a web application. Availability of one or more services or application instances running as services is indicated in the graphical user interface using different states of the context-sensitive icon. A user may select the icon corresponding to an available and compatible service to be applied to the currently active work in the web application. A single user gesture type command releases the corresponding service.” Abstract.]
Klein and Subramanian pertain to query/command directed for performance of a task to a machine and the use of context as part of the information associated with query/command. It would have been obvious to combine the availability indicator of Subramanian with the system Klein to provide a visual indicator to the user. This combination falls under combining prior art elements according to known methods to yield predictable results or use of known technique to improve similar devices (methods, or products) in the same way. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See also: Agarwal (U.S. 20200184956), Knapp (U.S. 20220308828), and Sarikaya (U.S. 11929070).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARIBA SIRJANI whose telephone number is (571)270-1499. The examiner can normally be reached 9 to 5, M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Desir can be reached at 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Fariba Sirjani/
Primary Examiner, Art Unit 2659
Prosecution Insights