DETAILED ACTION
This Office action is in response to the applicant's filing of 03/07/2025.
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 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 03/07/2025 and 08/04/2025 have been considered by the examiner.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1-20 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1-10 recite a system for real-time bidding and claims 11-20 recite a method for real-time bidding. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, claims 1 and 11 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1 and 11 recite limitations directed to the abstract idea including “receiving user input data; generates a predicted expected performance based on the user input data; and adjusting at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance.” These further limitations are not seen as any more than the judicial exception. Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted expected performance based on the user input data.” The claims are considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as adjusting bids of keywords and product based on predicted expected performance. The claims are also considered to be an abstract idea under Mental Processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the claims are directed to receiving data (i.e. input data); predicting data (i.e. expected performance); and adjusting data (i.e. bids based on predicted expected performance). Therefore, under Step 2A, Prong I, claims 1 and 11 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted expected performance based on the user input data.” These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception (i.e. adjusting bids based on predicted expected performance) to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1 and 11 recite additional limitations including “generating a first machine learning model that generates a predicted expected performance based on the user input data.” These limitations do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1 and 11 do not include additional elements or a combination of elements that result in the claims amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe a “any machine learning or neural network methodology known in the art or developed in the future”, ¶ [0107], for implementing the machine learning model, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 2-10 and 12-20 further recite the method of claim 1 and system of claim 11, respectively. Dependent claims 2-10 and 12-20 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1 and 11. For example, claims 2-4, 10 and 12-14, 20 further describe the limitations for the user input that is used to predict expected performance in order to adjust bids– which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-10 and 12-20, there are no additional elements introduced. For example, dependent claims 5-10 and 15-20 further describe the type of machine learning model being used. These additional limitations are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use (i.e. machine learning model) and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,).
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
Claim Rejections - 35 USC § 102(a)(1)
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.
Claim(s) 1-4, 7, 10-14, 17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Publication 2015/0066661 to Bhattacharjee.
Claims 1-10 and 11-20 are system and method claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 1:
Bhattacharjee teaches:
A real-time bidding system comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to (Bhattacharjee: ¶ [0121]):
receive user input data (i.e. receive user’s bid for keywords) (Bhattacharjee: ¶ [0042] “The mechanics of this text ads channel often requires such users to select a set of keywords, bid on those keywords, set up the appropriate creative and landing pages, and advertise to the right users, as well as perform other tasks. Optimal choices need to be made in all these dimensions ( e.g., keyword selection, bidding, landing page) in order to profitably bring in the right set of people to the website.” Furthermore, as cited in ¶ [0052] “In some embodiments, for any keyword k in the user's portfolio, the bid k is generated by an optimization routine Opt( ... ), which can depend on any combination of various parameters:”);
generate a first machine learning model that generates a predicted expected performance based on the user input data (i.e. generating a machine learning model that generates expected revenue or performance for each keyword based on user input) (Bhattacharjee: ¶¶ [0052] [0053] “In some embodiments, for any keyword k in the user's portfolio, the bid k is generated by an optimization routine Opt( ... ), which can depend on any combination of various parameters:… Some of the parameters, such as the RPC and the CPC, can be learned by a machine learning based prediction model. In some embodiments, an RPC prediction model 410 can be used to supply the expected RPC for any keyword.”); and
adjust at least one bid on at least one of at least one keyword and at least one product associated with at least one marketplace, based on the predicted expected performance (i.e. adjust bid on keywords based on expected revenue or performance, wherein keywords relate to products associated with marketplace) (Bhattacharjee: ¶¶ [0104] [0105] “In some embodiments, the exposure determination can be based on a determination that a ratio of a number of impressions for the published content on the search engine to a total search volume on the search engine for a specified period of time is less than a predetermined value. The exposure determination can be based on a determination that a click-through rate for the keyword on the search engine is a predetermined amount greater than a click-through rate for a population of keywords on the search engine. The exposure determination can be based on a determination that a predetermined amount of inventory on an online marketplace of the user has a predetermined level of affinity with the keyword. The exposure determination can be based on a determination that a period-based budget of the user has increased by at least a predetermined amount…In some embodiments, the update operation can comprise adjusting the corresponding bid for a keyword based on an external suggestion from an API of a search engine.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”).
With respect to Claim 11:
All limitations as recited have been analyzed and rejected to claim 1. Claim 11 recites “A real-time bidding method comprising:” the steps of system claim 1. Claim 11 does not teach or define any new limitations beyond claim 1. Therefore it is rejected under the same rationale.
With respect to Claim 2:
Bhattacharjee teaches:
The system of claim 1, wherein the user input data includes a desired total bid value and a bid period (i.e. user’s input bid includes user’s daily budget for the bid, wherein the budget represents desired total bid value and daily or other time period represents bid period) (Bhattacharjee: ¶ [0057] “In yet another example, a determined variation in a daily budget of a user can inform the bid optimization system 400 of an exposure appetite of a user for a keyword. For example, if the user has increased a daily budget by at least a certain degree or amount within a predetermined period of time for a portfolio of keywords that has remained the same or within a predetermined degree of similarity ( e.g., at least 95% of the keywords in the portfolio have remained the same from the time the daily budget was increased), then the bid optimization system 400 can interpret such an occurrence as an indication that the user wants to increase the exposure level of the keywords in the user's portfolio.”).
With respect to Claim 12:
All limitations as recited have been analyzed and rejected to claim 2. Claim 12 does not teach or define any new limitations beyond claim 2. Therefore it is rejected under the same rationale.
With respect to Claim 3:
Bhattacharjee teaches:
The system of claim 1, wherein the user input data includes information associated the at least one product (i.e. user’s input includes bids related to product) (Bhattacharjee: ¶ [0059] “The parameter "eventCalendar" can comprise indications of events associated with a desired increase in exposure. For example, an indication of a particular holiday, such as Christmas, can be used by the bid optimization system 400 to increase a bid for a keyword having an association with that holiday, in order to increase its exposure and take advantage of the event.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”).
With respect to Claim 13:
All limitations as recited have been analyzed and rejected to claim 3. Claim 13 does not teach or define any new limitations beyond claim 3. Therefore it is rejected under the same rationale.
With respect to Claim 4:
Bhattacharjee teaches:
The system of claim 1, wherein the user input includes information associated with the at least one marketplace (i.e. user’s input includes bids related to product in marketplace) (Bhattacharjee: ¶ [0059] “The parameter "eventCalendar" can comprise indications of events associated with a desired increase in exposure. For example, an indication of a particular holiday, such as Christmas, can be used by the bid optimization system 400 to increase a bid for a keyword having an association with that holiday, in order to increase its exposure and take advantage of the event.” Furthermore, as cited in ¶ [0064] “In some embodiments, the event calendar information includes, but is not limited to, information about future events that could influence the effectiveness, and therefore value, of a keyword. Examples of such future events include, but are not limited to, holidays (e.g., certain keywords can be more effective around Christmas time) and product launch events (e.g., certain keywords can be more effective around the time that a new version of a smartphone is released). The event calendar information can be used by the bid optimization system 400 in its determination of optimal bids for keywords.”).
With respect to Claim 14:
All limitations as recited have been analyzed and rejected to claim 4. Claim 14 does not teach or define any new limitations beyond claim 4. Therefore it is rejected under the same rationale.
With respect to Claim 7:
Bhattacharjee teaches:
The system of claim 1, wherein the instructions further cause the processor to generate a second machine learning model that generates a predicted expected cost based on the user input (i.e. generate CPC predictive model that generates predict cost per conversion) (Bhattacharjee: ¶ [0061] “A predicted RPC and a predicted CPC can be determined using an RPC prediction model 410 and a CPC prediction model 420, respectively. The predicted RPC and the predicted CPC can be determined using daily or other period-based feedback about the performance results of the corresponding keyword on one or more search engines 470. This feedback can be provided by the search engine(s) 470 in the form of one or more daily or other period-based search reports 480. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be a part of or otherwise incorporated into the bid optimization system 400. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be implemented as their own modules, distinct from the bid optimization system 400.”).
With respect to Claim 17:
All limitations as recited have been analyzed and rejected to claim 7. Claim 17 does not teach or define any new limitations beyond claim 7. Therefore it is rejected under the same rationale.
With respect to Claim 10:
Bhattacharjee teaches:
The system of claim 7, wherein the instructions further cause the processor to adjust the at least one bid on the at least one of at least one keyword and the at least one product associated with the at least one marketplace, further based on the predicted expected cost (i.e. bids are optimized/adjusted on keywords/products based on CPC prediction model) (Bhattacharjee: ¶ [0061] “Referring back to FIG. 4, the bid optimization system 400 can use any combination of the above-mentioned parameters to determine optimal bids for keywords. A predicted RPC and a predicted CPC can be determined using an RPC prediction model 410 and a CPC prediction model 420, respectively. The predicted RPC and the predicted CPC can be determined using daily or other period-based feedback about the performance results of the corresponding keyword on one or more search engines 470. This feedback can be provided by the search engine(s) 470 in the form of one or more daily or other period-based search reports 480. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be a part of or otherwise incorporated into the bid optimization system 400. In some embodiments, the RPC prediction model 410 and the CPC prediction model 420 can be implemented as their own modules, distinct from the bid optimization system 400.” Furthermore, as cited in ¶ [0077] “bid update - if predicted RPC > predicted CPC, increase bid; else, decrease bid.”).
With respect to Claim 20:
All limitations as recited have been analyzed and rejected to claim 10. Claim 20 does not teach or define any new limitations beyond claim 10. Therefore it is rejected under the same rationale.
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.
Claim(s) 5, 6, 8, 9, 15, 16, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharjee in view of U.S. Publication 2014/0289017 to Trenkle.
With respect to Claim 5:
Bhattacharjee does not explicitly disclose the system of claim 1, wherein the first machine learning model includes a tree-based machine learning model.
However, Trenkle further discloses wherein the first machine learning model includes a tree-based machine learning model (i.e. machine learning model is decision tree model) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s first machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a first machine learning model that generates a predicted expected performance based on the user input data (See claim 1). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 15:
All limitations as recited have been analyzed and rejected to claim 5. Claim 15 does not teach or define any new limitations beyond claim 5. Therefore it is rejected under the same rationale.
With respect to Claim 6:
Bhattacharjee does not explicitly disclose the system of claim 1, wherein the first machine learning model includes a Bayesian machine learning model.
However, Trenkle further discloses wherein the first machine learning model includes a Bayesian machine learning model (i.e. machine learning model utilizes Bayesian techniques) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s first machine learning model includes a Bayesian machine learning model to Bhattacharjee’s generate a first machine learning model that generates a predicted expected performance based on the user input data (See claim 1). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 16:
All limitations as recited have been analyzed and rejected to claim 6. Claim 16 does not teach or define any new limitations beyond claim 6. Therefore it is rejected under the same rationale.
With respect to Claim 8:
Bhattacharjee does not explicitly disclose the system of claim 7, wherein the second machine learning model includes a tree-based machine learning model.
However, Trenkle further discloses wherein the second machine learning model includes a tree-based machine learning model (i.e. machine learning model is decision tree model, wherein a many machine learning models may be utilized) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s second machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a second machine learning model that generates a predicted expected cost based on the user input (See claim 7). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 18:
All limitations as recited have been analyzed and rejected to claim 8. Claim 18 does not teach or define any new limitations beyond claim 8. Therefore it is rejected under the same rationale.
With respect to Claim 9:
Bhattacharjee does not explicitly disclose the system of claim 7, wherein the second machine learning model includes a Bayesian machine learning model.
However, Trenkle further discloses wherein the second machine learning model includes a Bayesian machine learning model (i.e. machine learning model utilizes Bayesian techniques, wherein a many machine learning models may be utilized) (Trenkle: ¶ [0119] “This signature database 1306 is then processed by model building engine 1308 to produce a database of categorization models 1310. Model building engine 1308 may comprise for example any of a number of machine learning analysis engines known in the art, including for example but not limited to Decision Trees and Ensembles, Neural Nets, Deep Neural Nets, Support Vector Machines, K-Nearest Neighbors, and Bayesian techniques. Once the database of models 1310 has been constructed, unknown viewers can then be categorized relative to a spectrum of specific demographic categories as described for example above. For each demographic category segment, a specific model is generated. Thus, there will be as many models as there are demographic category segments of interest.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Trenkle’s second machine learning model includes a tree-based machine learning model to Bhattacharjee’s generate a second machine learning model that generates a predicted expected cost based on the user input (See claim 7). One of ordinary skill in the art would have been motivated to do so because “The modeling processes described herein based on machine learning enable viewers to be characterized more accurately relative to belonging to targeted demographic segments. This more accurate targeting is used in the bidding process for online ad opportunities in order to enable campaign budgets to be used more effectively.” (Trenkle: ¶ [0123]).
With respect to Claim 19:
All limitations as recited have been analyzed and rejected to claim 9. Claim 19 does not teach or define any new limitations beyond claim 9. Therefore it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are cited to further show the state of the art:
U.S. Publication 2020/0219145 to Kalampoukas for disclosing A system and method for adjusting bid forming in a realtime bidding advertisement auction system. The method may be implemented for a bidding agent or in connection with a campaign database specifying campaign objectives by segment and campaign duration.
U.S. Publication 2023/0069621 to White for disclosing A method and system for enriching bid requests for real-time bidding on a digital advertisement placement are provided. The method comprises processing a received bid request to extract at least one data point, wherein the bid request is received from a website requesting placement of a digital advertisement; causing generation of at least one enriched data point based on the at least one extracted data point; and associating the at least one enriched data point with the received bid request to allow a refined real-time bidding on a placement of a digital advertisement in a webpage of the website in response to the bid on the received bid request.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pairdirect.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
February 5, 2026