DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the Request for Continued Examination filed on 05/08/2026.
Claims 1, 3, 15, and 20 have been amended and are hereby entered.
Claims 2 and 4 have been canceled.
Claims 1, 3, and 5-20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/08/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pages 7-13, filed 05/08/2026, with respect to the 35 U.S.C. 101 rejections of claims 1-20 have been fully considered but are generally not persuasive. The 35 U.S.C. 101 rejections of claims 1, 3, and 5-20 have been maintained, with the rejections of claims 2 and 4 rendered moot because of the cancellation of the claims.
Applicant first argues across pages 7-9 that the claims do not recite a judicial exception at Step 2A Prong One. Applicant’s arguments against the claims reciting a Mental Process rest on the claims now reciting the application of a machine learning model “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” (amended claim 1) such that the classification of the claims as reciting a Mental Process would allegedly be impermissibly oversimplifying the claims. Applicant argues that the training and iterative refinement of the machine learning model could not be done by a human with or without a physical aid, and that these limitations preclude the amended claims from reciting a Mental Process. Examiner respectfully disagrees.
First, Examiner notes that the claims as amended do not positively recite the training of the machine learning model on the normalized participating user information and the user preference information as being performed as part of the claimed methods and systems. Instead, the machine learning model that is being applied as part of generating the ranking information is described as having been trained as Applicant describes, but not being trained as part of the claimed method. Similarly, the claims as amended do not positively recite the iterative updating based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps. Instead, the machine learning model that is being applied as part of generating the ranking information is described as being iteratively updated as Applicant describes, but the iterative updating is not a positive part of the claimed method/system. Therefore, Examiner is not giving patentable weight to “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps”. Accordingly, the limitations reciting machine learning that are being given patentable weight are “wherein the generating of the ranking information comprises applying a machine-learning model”. While the claim citations in this paragraph have been take from claim 1, Examiner notes that similar analysis results in the analogous limitations of independent claims 15 and 20 also not being given patentable weight.
As the limitations regarding training and iterative updating are the features upon which Applicant relies in the argument against the claims reciting a Mental Process, the training and iterative updating limitations not having patentable weight makes Applicant’s arguments unpersuasive. Regarding the limitation of “wherein the generating of the ranking information comprises applying a machine-learning model” that has patentable weight, Examiner notes that this limitation does not preclude the claim from reciting a Mental Process.
MPEP 2106.04(a)(2) III.C. states that “Using a computer as a tool to perform a mental process” still recites a Mental Process. In the instant claims, the collection and normalization of historical communication information by segmenting the historical communication information into blocks of data based on type, collecting user preference information comprising ranked recommended communication modes, and analyzing the normalized participating user information and user preference information fall under Mental Process by a human with or without a physical aid for the reasons discussed on pages 4-6 of the 02/09/2026 Final Rejection. Similarly, generating ranking information of modes of communication based on the normalized participating user information and user preference information is a process that can be performed by a human with or without a physical aid (i.e. counting number of successful communications for several communication types in several time blocks and comparing with stated user preference information to generate a ranking). The instant claim is using a computer (a machine learning model) as a tool perform the Mental Process. Accordingly, when considered as a whole, the amended claims still recite a Mental Process.
Applicant also argues on page 9 of Remarks that at Step 2A Prong One the instant claims also allegedly do not recite a Certain Method of Organizing Human Activity. Applicant again cites to the iterative training of the machine learning model and argues that the system’s output comprises a communication mode recommendation is an “incidental fact” that does not recite a Certain Method of Organizing Human Activity. Examiner respectfully disagrees. As discussed above, the iterative training of the machine learning model is not given patentable weight in the present claims. Additionally, the output of the system being a communication mode recommendation is not an “incidental fact” as Applicant alleges, as specification [0002]-[0004] discuss how the use of a wrong communication method may lead to confusion, delayed communication, etc. Therefore, instead of a being “incidental”, the recommendation of a communication method directly addresses the missed communications stated in Applicant’s specification. Furthermore, Examiner notes the type of Certain Methods of Organizing Human Activity that the claims are classified as is “Managing Personal Behavior or Relationships or Interactions Between People”. MPEP 2106.04(a)(2) II.C. states “The sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions”. Therefore, in contrast to Applicant’s analogy regarding a search engine presenting results, the claimed invention is managing an interaction between two people by guiding the people to a communication mode preferred by the parties. Regarding Applicant’s argument that the system itself is improved, Examiner notes that the machine learning discussed in the specification in paragraph [0039] does not recite any improvement to the machine learning itself but rather that the machine learning “can be used”. While technical improvement arguments will be discussed in greater detail below, Applicant arguments that the claims do not recite a judicial exception at Step 2A Prong One are not persuasive.
Next, Applicant argues across pages 9-12 that the amended claims integrate their judicial exceptions into a practical application. Applicant argues that the claimed invention is a technical improvement over the static “status indicators” considered in the specification. Applicant argues that the replacement of static indicators with “an iteratively self-improving machine learning architecture” is a specific technological solution to the static indicators. Applicant goes on to argue that the “iteratively self-improving” machine learning model makes the instant claims eligible by way of reasoning articulated in the August 4th Memo and the collection of case law cited on pages 10-11. Applicant particularly argues that the claimed invention is eligible for an improvement “as to how the machine-learning model itself operates” as in Desjardins. Examiner respectfully disagrees. First, Examiner notes again that the training and iterative updating features relied upon by Applicant throughout the arguments are not positively recited in the claims and are thus not being given patentable weight for the reasons discussed above. Accordingly, Applicant’s arguments about the iterative updating and training of the machine learning model are not persuasive.
However, even if these features were to be given patentable weight, Applicant’s arguments would still be unpersuasive. First, regarding the status indicator argument, the status indicators are providing an indication based on one set of abstract data (i.e. whether a person is “available”, “unavailable”, etc.). In the instant invention, the indication of recommended communication modes is achieved by considering additional sets of abstract data (i.e. what was the mode of communication during a successful connection?, when did the successful communication occur?, how many instances of successful/failing communication occurred?, what communication modes does a user state that they prefer?, etc.). Therefore, the improvement of a recommended communication mode over the static status indicators is not a technical one (i.e. an improvement to how the indicator is displayed on an interface, etc.), but is instead an improvement to the abstract idea by considering additional abstract data beyond a single status and providing a correspondingly more substantive answer of particular communication modes that a user would prefer. MPEP 2106.05(a) recites “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. Applicant’s specification does not recite a technical challenge being overcome to collect this more detailed communication data that is then manipulated to arrive at ranked list of communication modes. Accordingly, the collection and use of additional data to arrive at ranked recommended communication modes is not a technical improvement over status indicators.
Regarding the newly added machine learning, even assuming, arguendo, that the features argued by Applicant were to be given patentable weight, the machine learning does not amount to a technical improvement analogous to the cited case law. Particularly, Examiner disagrees with Applicant’s argument that the claimed invention is eligible for similar reasoning to that discussed in Desjardins. Applicant’s apparent assertion that Desjardins found that iteratively adjusting model parameters is an overly broad interpretation. Desjardins particularly states that claims reflect “improvements in training the machine learning model itself” (Page 8). Therefore, improvements to the training process of machine learning models may be eligible in line with Desjardins, but the present claims recite repeated training instead of improved training. Therefore, the current claims are not analogous to Desjardins as they are merely applying a machine learning model instead of improving the functioning of a machine learning model.
Regarding Enfish, the application of a trained, iteratively updated machine learning model is not analogous to the improvement of the functioning of a computer itself like Enfish. Neither the instant claims nor the specification, particularly [0039], indicate an improvement to the functioning of machine learning models themselves, instead merely indicating that machine learning models can be broadly applied to determine the ranking in the claims. This is in contrast to the particular self-referential table improvement of Enfish. Regarding McRO, McRO was found eligible because “the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans” (MPEP 2106.05(a)). In contrast, the machine learning model of the claimed invention is not automating a process that was limited to subjective implementation by humans. The claims and specification do not indicate that the machine learning model enabled automation of some process previously only done by humans. Specification [0039] merely states that “Similarly, system 200 can use machine learning to predict preferred modes, times, or the like of communications between two or more users”, providing machine learning models as an alternative method rather than indicating some improvement to automate a process only previously done manually. Regarding Cosmokey, the invention at issue in that case explicitly recited specific steps/timings for activating and deactivating authentication functions to overcome the technical problem of hacking (Page 14). Nothing analogous to such activation/deactivation of functions is found in the instant claims, and the problem being solved in the claimed invention is suggesting communication methods to users, not a technical problem. Finally, regarding Cooperative Entertainment, the instant invention does not recite an improvement to a peer-to-peer network or communication network like Applicant argues. Instead of providing a technical improvement to a communication network, the claimed invention is instead suggesting modes of communication to users to increase user satisfaction and guide users to their preferred methods of communication. The generating of the rankings by applying a machine learning model is using a machine learning model as a tool to accomplish this goal of providing suggested modes of communication. The invention is not directed to an improvement in the machine learning model itself.
Instead, even assuming arguendo that all of the machine learning model description were to be given patentable weight, the machine learning model falls into the analysis of MPEP 2106.05(f). Examiner particularly points to “(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"” from this section of the MPEP. In the case of the instant claims, the claims recite that generating the ranking information “comprises applying a machine-learning model…” without reciting how the machine learning model is used in the process of generating the ranking information. Accordingly, the instant claims are reciting that desired output of the machine learning model is ranking information without describing how the machine learning model arrives at the desired output. Additionally, the application of a machine learning model to generate the ranking information is using the machine learning model as a tool to perform the judicial exception of ranking suggested communication methods to present to a user. Therefore, instead of integrating the judicial exceptions into a practical application at Step 2A Prong Two by providing a technical improvement, the additional elements of the claims, evaluated as a whole, amount to instructions to apply the judicial exceptions. Applicant’s Prong Two arguments are not persuasive.
Lastly, Applicant argues on pages 12-13 that the amended claims provide an inventive concept at Step 2B. Applicant argues that the particular combination of limitations are unconventional and thus provide an inventive concept at Step 2B. Examiner respectfully disagrees. First, Examiner again notes here that the training and iterative updating features that Applicant argues provide “a specific technical architecture” are not being given patentable weight for the reasoning discussed above. Accordingly, Applicant’s arguments regarding the iterative and self-updating nature of the machine learning model are not persuasive. Even if these descriptors of the model were to be given patentable weight arguendo, the claims would still not amount to significantly more at Step 2B. Regarding claim 1, the additional elements of the method being “electronic”, a data collection and normalization engine, a database, and a data service module are generic computing components/implementation that are operating in their normal capacity to perform the abstract idea. Examiner notes particularly regarding the “engines” and “modules”, under their 112(f) interpretation structure from the specification in [0032] merely requires that these “modules” and “engines” be instructions on a computer storage medium that are executed on a server. Nothing in Applicant’s specification indicates that the arrangement of these elements provides significantly more. The specification reciting that static indicators are not used in the instant invention does not mean that the particular architecture of the invention provides significantly more. Even when including the machine learning model into the ordered combination, the additional elements are still being used as tools to perform the judicial exception and amount to instructions to apply the judicial exception. Per MPEP 2106.05(f), “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"” (emphasis added). Similar analysis holds for the similarly amended independent claims 15 and 20. Applicant’s arguments at Step 2B are therefore unpersuasive. The claims remain rejected under 35 U.S.C. 101.
Applicant’s arguments, see pages 13-17, filed 11/05/2025, with respect to the 35 U.S.C. 103 rejection of claim 1 have been fully considered but are either unpersuasive or moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim 1 still stands rejected under 35 U.S.C. 103.
After summarizing the rejections on pages 13-14, Applicant argues on pages 14-15 that Brenna allegedly does not teach the limitations of: "normalizing the historical communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information," and "using a data service module, analyzing the normalized participating user information and the user preference information and generating ranking information comprising recommended modes of communication for the participating user based on the analysis".
Regarding normalization of the data, Examiner notes Brenna’s teachings read on “normalization” under broadest reasonable interpretation in light of Applicant’s specification. Particularly, Examiner notes that specification [0016]-[0018] consider “normalization” to cover splitting collected data regarding communications into time periods. Brenna [0030] cited in the previous rejection of claim 1 explicitly recites that the number of communications being tallied have occurred “since the start of corresponding interval”, which Brenna [0062]-[0063] state is a time interval. Therefore, collecting and tallying communication in a time interval as taught in Brenna reads on “normalizing” the data in light of Applicant’s specification. While Examiner acknowledged in the previous rejection that Brenna does not explicitly teach such normalization being segmenting the data into multiple blocks of data based on a type of historical communication information, O'Shaughnessy was and is used to teach this feature.
Regarding the limitation of "using a data service module, analyzing the normalized participating user information and the user preference information and generating ranking information comprising recommended modes of communication for the participating user based on the analysis", Brenna teaches that the normalized communication data discussed above is analyzed by the inference extractor to determined a ranked list of preferred communication modes for the recipient user in [0051]-[0052]. As such, the normalized participating user information discussed above is being analyzed by the inference extractor, which reads on a data service module because the inference extractor is an application loaded onto a memory and executed by the processing system of Brenna [0081], to generate a ranked list of preferred communication modes. Regarding the user preference information also being analyzed along with the participant user information, Examiner notes that the previous rejection stated that the user preference information of Brenna was not analyzed alongside the normalized participating user information. While Céret et al. (U.S. Pre-Grant Publication No. 2017/0053209, hereafter known as Céret) was used to teach this limitation previously, Karp et al. (U.S. Patent No. 10,63,0840; hereafter known as Karp) is now being used to teach this limitation as will be discussed below.
Finally, Applicant’s argues that Brenna does not teach the limitation of "applying a machine-learning model trained on the normalized participating user information and the user preference information, the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps”. For the reasoning discussed above, the limitations of “trained on the normalized participating user information and the user preference information” and “the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight in the rejections below. Regarding the application of a machine learning model, which is being given patentable weight, Applicant’s arguments against Brenna are moot because Karp is used to teach this limitation below.
Next, Applicant argues across pages 15-17 that claim 1 is also distinguished over O'Shaughnessy. Applicant particularly argues that O'Shaughnessy fails to teach "collecting historical communication information from two or more modes of communication for a participating user" and "generating ranking information comprising recommended modes of communication for the participating user based on the analysis [of the participating user's historical communication information]".
Regarding "collecting historical communication information from two or more modes of communication for a participating user", Applicant’s argument is moot because the primary reference of Brenna is used to teach this limitation, just like Brenna was used to teach this limitation when it was the now-canceled claim 2. Specifically, Brenna [0030] teaches collecting communication data for a user “John” across communication modes including phone calls, text-chats, and emails. Brenna [0031] and [0035] discuss how John’s communication preferences are the preferences of the participating user when a colleague attempts to contact him, see at least [0035] “John's communication preference information may be presented in association with John's contact card to Paul through a UI of Paul's email application and to Sarah through a UI of a voice/video and chat application. In this way, Sarah and Paul may be informed of John's communication mode preference at the time that they are considering initiating a communication to John”. Therefore, O'Shaughnessy is not relied upon to teach this limitation argued by Applicant.
Regarding the generation of ranking information limitation, O'Shaughnessy was not used to teach this limitation in the previous office action and is not used here. As discussed in the rejection below, Brenna teaches the use of normalized participant communication data to generate rankings. While the combination of Brenna and O'Shaughnessy does not teach the generation of rankings by analyzing both normalized participating user information and user preference information, Karp is used to teach the analysis by applying machine learning. Therefore, O'Shaughnessy is not relied upon to teach this limitation argued by Applicant.
Examiner notes that in claim 1 O'Shaughnessy is cited to teach the segmenting of communication history data into blocks and based on a type of communication information, while Brenna teaches the initial collecting of the recipient user communication data. In other words, while Brenna teaches all the participating user communication information collected in one interval, O'Shaughnessy teaches that such communication history information can be segmented into blocks of N minutes as well as being segmented by type of communication. While O'Shaughnessy teaches that communication history is obtained from the initiator’s device, the historical communication data being collected and normalized in O'Shaughnessy is that between the initiator and the recipient. Examiner notes that this historical communication data between the initiator and the recipient is analogous to the historical communication data received in Brenna from the recipient device (i.e. the data from Brenna [0030] reciting the number of time a recipient and initiator communicated using particular modes of communication). While the source of the data may be different devices in Brenna and O'Shaughnessy, one of ordinary skill in the art would have still recognized that the type of data obtained by Brenna could be segmented into N minute time blocks and based on communication type as taught in O'Shaughnessy. One of ordinary skill in the art would have further had motivation to apply this segmentation process to Brenna’s data for the reasoning stated in the rejection below. Namely, that dividing the data into N minute blocks allows for greater consideration of the impact of the time of day on communication preferences than Brenna alone. Therefore, Applicant’s arguments that O'Shaughnessy retrieving data from a different location would preclude Brenna and O'Shaughnessy from being combined are not persuasive.
Regarding the final limitation of claim 1 in which recipient preferences are sent to an initiating user, Examiner notes that the [0035] citation above and [0073] (in the reverse case where John is attempting to contact Paul) of Brenna further teach the final limitation of claim 1. Applicant argues this limitation is not taught by O'Shaughnessy on page 16 of remarks, but O'Shaughnessy was not used to teach this limitation in the previous office action. Applicant’s arguments regarding the final limitation of claim 1 not being taught by O'Shaughnessy are therefore moot.
Finally, Applicant argues on page 17 that the combination of Brenna, O'Shaughnessy, and Céret do not teach the newly incorporated machine learning model of claim 1. This argument is moot, as Karp et al. (U.S. Patent No. 10,63,0840; hereafter known as Karp) is now used in place of Céret to teach the machine learning features as discussed below. Examiner again notes here that the limitations of “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight for the reasoning discussed above.
Regarding Applicant’s arguments against independent claims 15 and 20 across pages 17-18 of remarks, Applicant’s arguments are not persuasive for similar reasoning as discussed above regarding claim 1. Applicant’s arguments on page 18 that all dependent claims have overcome the art by virtue of their dependence on their respective independent claims are also not persuasive, as the independent claims have not overcome the art. Accordingly, claims 1, 3, and 5-20 still stand rejected under 35 U.S.C. 103.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“a data collection and normalization engine” in claims 1, 15, 16, and 20
“a data service module” in claims 1, 7, 15, 17, and 20
“a recommendation engine” in claims 10, 15, and 18
“a sentiment analysis engine” in claim 19
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Regarding the structure in the specification, structure can be found in paragraphs [0031]-[0033] and [0035]-[0038].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 3, and 5-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim has been amended to recite “wherein the generating of the ranking information comprises applying a machine-learning model trained on the normalized participating user information and the user preference information, the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps”. Applicant’s specification as-filed discusses machine learning in paragraphs [0011] “This may involve machine learning to predict the best or most desirable conditions (e.g., mode, time, etc.) for the communication”, [0038] “In some cases, sentiment analysis engine 212 can perform machine learning and update words, phrases, and/or the like associated with the words and phrases based on the learning”, and [0039] “Similarly, system 200 can use machine learning to predict preferred modes, times, or the like of communications between two or more users”.
As can be seen above, paragraph [0038] discusses machine learning in the context of sentiment analysis, not the ranking of modes of communication. While [0011] and [0039] consider using machine learning “to predict” preferred communication modes, nowhere in the specification as-filed is the machine learning model recited as being applied to generate ranking information as has been newly introduced into amended claim 1. Additionally, the specification as-filed does not consider the training of the machine learning model, either by the claimed invention or by some other external entity, anywhere. Finally, while paragraphs [0016]-[0019] recite that communication can be collected and normalized repeatedly, nowhere in the specification is it recited that part of this looping collection/normalization process is the iterative updating of the machine learning model. One of ordinary skill in the art, in view of the specification as filed, would not have recognized that Applicant possessed a machine learning model trained on the normalized participating user information and the user preference information, iteratively updated as subsequent historical communication information collected during repeated executions of the collecting and normalizing steps becomes available. Accordingly, claim 1 lacks sufficient written description support for: applying a machine learning model to generate ranking information, the machine learning model being trained on the normalized participating user information and the user preference information, and the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps. Claim 1 therefore lacks sufficient written description and is rejected under 35 U.S.C. 112(a).
Examiner notes that, as discussed in the Response to Arguments section above, the limitations of “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight, as the limitations of training the machine learning model and iteratively updating the machine learning model are not positively recited steps of the claimed invention but instead describe the machine learning model. While these limitations are not being given patentable weight, they still remain unsupported by the specification as-filed for the reasoning discussed above.
Other independent claims 15 and 20 also lack sufficient written description support for similar reasoning as discussed above. Claim 15 lacks sufficient written description support for: using a machine-learning model to analyze the normalized participating user information, a machine-learning model trained on the normalized participating user information and the participant user preference information, and the machine-learning model being iteratively updated based on subsequently received historical participant user communication information. Claim 20 lacks sufficient written description support for: using a machine-learning model to analyze the normalized participating user information, a machine-learning model trained on the normalized participating user information and the participant user preference information, and the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps.
Dependent claims 3, 5-14, and 16-19 lack sufficient written description support and have been rejected under 35 U.S.C. 112(a) by virtue of their dependence on their respective independent claims.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 3 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 3 has been amended to depend from itself (“3. (Currently Amended) The electronic communication method of claim [[2]]3, wherein…” As such, claim 3 does not contain a reference to and further limit a claim previously set forth as required by 35 U.S.C. 112(d). Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Examiner suggests amending claim 3 to depend from independent claim 1, as appears was Applicant’s intent with the present amendment. For the purposes of examination, Examiner will be interpreting claim 3 as if it depended upon claim 1 when discussing the rejections below.
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, 3, and 5-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite recommending a method of communication to an initiating user to contact a participating user.
As an initial matter, claims 1-3, and 5-14 fall into at least the process category of statutory subject matter. Claims 15-19 fall into at least the machine category of statutory subject matter. Finally, claim 20 falls into at least the process category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
In claim 1, the limitation of “An electronic communication method comprising the steps of: collecting historical communication information from two or more modes of communication for a participating user”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “electronic communication method,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “using a data collection and normalization engine, normalizing the historical communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information; collecting user preference information for the participating user, wherein the user preference information comprises ranked recommended modes of communication; storing the normalized participating user information in a database; storing the user preference information for the participating user in the database; using a data service module, analyzing the normalized participating user information and the user preference information and generating ranking information comprising recommended modes of communication for the participating user based on the analysis, wherein the generating of the ranking information comprises applying a machine-learning model trained on the normalized participating user information and the user preference information, the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps; and sending one or more recommended modes of communication to an initiating user based on the ranking information”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 1 recites the concept of recommending a method of communication to an initiating user to contact a participating user which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. A communication method comprising the steps of: collecting historical communication information from two or more modes of communication for a participating user; normalizing the historical communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information; collecting user preference information for the participating user; storing the normalized participating user information, wherein the user preference information comprises ranked recommended modes of communication; storing the user preference information for the participating user; analyzing the normalized participating user information and the user preference information and generating ranking information comprising recommended modes of communication for the participating user based on the analysis; and sending one or more recommended modes of communication to an initiating user based on the ranking information all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of the method being “electronic”, a data collection and normalization engine, a database, a data service module, and applying a machine-learning model. Examiner is not giving patentable weight to “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps”. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to 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 of the method being “electronic”, a data collection and normalization engine, a database, a data service module, and applying a machine-learning model amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 3 and 5-8 further limit the abstract idea of claim 1 (Examiner is interpreting claim 3 as if it were to depend on claim 1 per the 35 U.S.C. 112(d) section above) without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 9 further limits the abstract idea of claim 8 while introducing the additional element of automatically connecting an initiating user device to a communication with a participant user device. The claim does not integrate the abstract idea into a practical application because the element of automatically connecting an initiating user device to a communication with a participant user device is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 8 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 10 further limits the abstract idea of claim 1 while introducing the additional element of a recommendation engine. The claim does not integrate the abstract idea into a practical application because the element of a recommendation engine is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 11 further limits the abstract idea of claim 1 while introducing the additional elements of an initiating user device and a participating user device. The claim does not integrate the abstract idea into a practical application because the elements of an initiating user device and a participating user device are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. Adding these new additional elements into the additional elements from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 12 further limits the abstract idea of claim 11 while introducing the additional element of automatically connecting an initiating user device to a communication with a participant user device. The claim does not integrate the abstract idea into a practical application because the element of automatically connecting an initiating user device to a communication with a participant user device is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 11 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 13-14 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
In claim 15, the limitation of “An electronic communication system comprising: a data collection and normalization engine configured to receive historical participant user communication information from two or more modes of communication and to normalize the historical participant user communication information by segmenting the historical participant user communication information into blocks of data based on a type of the historical participant user communication information to provide normalized participating user information”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “electronic communication system” and “a data collection and normalization engine,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “a database comprising the normalized participating user information and participant user preference information, wherein the participant user preference information comprises ranked recommended modes of communication; a data service module configured to receive and analyze the normalized participating user information using a machine-learning model trained on the normalized participating user information and the participant user preference information, the machine-learning model being iteratively updated based on subsequently received historical participant user communication information, and to generate ranking of recommended modes of communication information for the participating user based on the analysis of the normalized participating user information; and a recommendation engine to receive the ranking of recommended modes of communication information and the participant user preference information and to provide a recommended mode of communication to an initiating user device”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 15 recites the concept of recommending a method of communication to an initiating user to contact a participating user which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. Receive historical participant user communication information from two or more modes of communication and to normalize the historical participant user communication information by segmenting the historical participant user communication information into blocks of data based on a type of the historical participant user communication information to provide normalized participating user information; the normalized participating user information and participant user preference information, wherein the participant user preference information comprises ranked recommended modes of communication; receive and analyze the normalized participating user information and to generate ranking of recommended modes of communication information for the participating user based on the analysis of the normalized participating user information; and receive the ranking of recommended modes of communication information and the participant user preference information and to provide a recommended mode of communication to an initiating user all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an electronic communication system, a data collection and normalization engine, a database, a data service module, a recommendation engine, an initiating user device, and a machine-learning model. Examiner notes that the limitations of “trained on the normalized participating user information and the participant user preference information” and “the machine-learning model being iteratively updated based on subsequently received historical participant user communication information” are not being given patentable weight. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to 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 of an electronic communication system, a data collection and normalization engine, a database, a data service module, a recommendation engine, an initiating user device, and a machine-learning model amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 16 further limits the abstract idea of claim 15 while introducing the additional element of a communication server. The claim does not integrate the abstract idea into a practical application because the element of a communication server is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 15 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 17 further limits the abstract idea of claim 15 while introducing the additional element of a communication server. The claim does not integrate the abstract idea into a practical application because the element of a communication server is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 15 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 18 further limits the abstract idea of claim 15 while introducing the additional element of a communication server. The claim does not integrate the abstract idea into a practical application because the element of a communication server is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 15 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 19 further limits the abstract idea of claim 15 while introducing the additional element of a sentiment analysis engine. The claim does not integrate the abstract idea into a practical application because the element of a sentiment analysis engine is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 15 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
In claim 20, the limitation of “An electronic communication method comprising: collecting communication information from two or more modes of communication for a participating user, the communication information comprising a plurality of modes of communication and one or more types of communication for each mode”, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “electronic communication method,” nothing in the claim element precludes the step from practically being performed in the mind. Similarly, the limitations of “using a data collection and normalization engine, normalizing the communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information; collecting user preference information for the participating user, wherein the user preference information comprises ranked recommended modes of communication; storing the normalized user information in a database; storing the user preference information in the database; using a data service module, analyzing the normalized participating user information using a machine-learning model trained on the normalized participating user information and the participant user preference information, the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps and generating ranking information of recommended modes of communication for the participating user and ranking information of recommended types of communication for the participating user based on the analysis; comparing the ranking information of recommended modes of communication and the ranking information of recommended types of communication for the participating user to the preference information; and sending a recommended mode and type of communication to an initiating user device”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Additionally, claim 20 recites the concept of recommending a method of communication to an initiating user to contact a participating user which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. A communication method comprising: collecting communication information from two or more modes of communication for a participating user, the communication information comprising a plurality of modes of communication and one or more types of communication for each mode; normalizing the communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information; collecting user preference information for the participating user, wherein the user preference information comprises ranked recommended modes of communication; storing the normalized user information; storing the user preference information; analyzing the normalized participating user information and generating ranking information of recommended modes of communication for the participating user and ranking information of recommended types of communication for the participating user based on the analysis; comparing the ranking information of recommended modes of communication and the ranking information of recommended types of communication for the participating user to the preference information; and sending a recommended mode and type of communication to an initiating user all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of the method being “electronic”, a data collection and normalization engine, a database, a data service module, an initiating user device, and a machine-learning model. Examiner notes that the limitations of “trained on the normalized participating user information and the participant user preference information” and “the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to 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 of the method being “electronic”, a data collection and normalization engine, a database, a data service module, an initiating user device, and a machine-learning model amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
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 1, 3, 5-7, 10-11, 13, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Brenna et al. (U.S. Pre-Grant Publication No. 2024/0037674, hereafter known as Brenna) in view of O'Shaughnessy et al. (U.S. Pre-Grant Publication No. 2009/0274286, hereafter known as O'Shaughnessy) and Karp et al. (U.S. Patent No. 10,630,840; hereafter known as Karp).
Regarding claim 1, Brenna teaches:
An electronic communication method comprising the steps of (see Fig. 6 and [0075]-[0079] for overall method of electronic communication)
collecting historical communication information from two or more modes of communication for a participating user (see [0075] "The operations 600 includes a tracking operation 602 that tracks communication activity of a first user conducted in association with a first communication account to a communication application (e.g., emails, chats, calls, meetings)" and [0028]-[0033] for the tracking of the sender, recipient, and modes of each communication, particularly [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114". See Fig. 3 and [0053]-[0058] for the communication tracker collecting data on the content of the messages)
using a data collection and normalization engine, normalizing the historical communication information for the participating user (see [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114" for normalizing the historical communication into counter data tallying the number of communications made using the communication tracker, which Examiner is interpreting as a data collection and normalization engine)
collecting user preference information for the participating user, wherein the user preference information comprises ranked recommended modes of communication (see Fig. 2 and [0038] "Configurable communication mode preferences 204 allow a user to set a preferred communication mode (e.g., voice, chat email) for different types of contacts (e.g., close contacts, new contacts, org-external contacts, org-internal contacts, all contacts, or “other”). Other implementations may allow the user to specify an ordered list of preferred communication modes, either to use as a default across all contacts or for use in association with certain types of contacts (e.g., “ContactType” as shown)" for an ordered list of preferred communication modes)
storing the normalized participating user information in a database (see [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient)" and [0045] "The system 300 includes a communication tracker 306 and an inference extractor 308 that perform at least some functions the same or similar to like-named components described with respect to FIG. 1" and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for storing the normalized information on application servers)
storing the user preference information for the participating user in the database (see [0024] "each user profile within the user profile datastore 112 includes communication preference, such as information relating to a user's preferred mode of communication and/or the specific content preferences. Updates to profile data within the user profile datastore 112 are affected by “pushing” profile data collected on user devices to the user profile datastore. For example, one or more of the communication application(s) 104 on the personal device 102 may update local user profile data 116 for John, such as responsive to alterations to profile settings that John manually configures or responsive to preference inferences extracted by the communication system 100" and [0067] "updated information within the user profile communication preferences 312 may be transmitted to a global user profile datastore (e.g., the user profile datastore 112 of FIG. 1)" and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for storing the user preference information on the application servers)
using a data service module, analyzing the normalized participating user information (see inference extractor 118 and 308 for the data service module. See [0051]-[0052] for the inference extractor accessing counts of communication modes and determining communication mode preferences. See [0030] "From this tracked information, the inference extractor 118 may infer that John's communication preferences for correspondence with Paul are, in descending order of preference: chat, voice, and email. Notably, this trend may be true across most or all of John's contacts. If so, a communication mode preference may be updated within the local user profile data 116 to indicate that, as a default, John's mode of communication preferences are, in descending order: chat, voice, and email regardless of sender identity" for accessing normalized communication information to generating ranking of recommended modes of communication)
and sending one or more recommended modes of communication to an initiating user based on the ranking information (see [0079] "A presentation operation 610 presents, on a user interface of the communication application, the inferred communication preference stored within a select one of the populated contact cards. For example, the inferred communication for a contact card of a first user is presented to a second user when the second user hovers over or clicks on the contact card of the first user" and Fig. 5 and [0073] "When John interacts with UI content corresponding to Paul's contact card 514 (e.g., a graphic including Paul's name, image, avatar), the contact card 514 is displayed. In this case, Paul's communication preferences are presented in a text box 516 that reads “Preferred communication method: Email, Chat, Voice calls, Video Meetings,” and the order is indicative of a preference from most preferred to least preferred")
While Brenna teaches normalizing historical communication data by determining counts of communication modes and participants in a communication session as discussed above, Brenna does not explicitly teach normalizing the historical communication information by segmenting the communication information into blocks based on the type of the historical communication information. Furthermore, while Brenna teaches receiving and storing user configurable communication preferences, Brenna does not explicitly teach analyzing the normalized participating user data along with the user configurable communication preferences to generate a ranking of communication modes by applying a machine learning model. O'Shaughnessy teaches:
using a data collection and normalization engine, normalizing the historical communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information (see Fig. 6 and [0090]-[0092] “At step 600, an initial mode is selected and at step 601 the call history data is analyzed for that particular selected mode to determine within a window of .+-.N minutes from the current time how many calls were made using the mode, covering a number of days specified by variable D…At step 602, this count is divided by D to give a short term average number of calls per day…A simple process finds a count for a larger period of H days and a long term average number of calls per day is calculated” and [0097] “Step 611 then determines whether there are further modes to be processed and if so the next mode is selected at step 613 and the process recommences from 601” for segmenting the communication data into blocks within N minutes of the current time over a plurality of days. See [0038] for different communication types including landline telephone, mobile telephone, push to talk telephone communication, etc. In combination with Brenna, the communication information is segmented into the time blocks as part of the normalization. See Applicant’s disclosure [0018] for grouping data into blocks of time as normalizing the data)
One of ordinary skill in the art would have recognized that applying the known technique of segmenting the historical user communication data into time blocks as part of the normalization of historical communication data of O'Shaughnessy to the system of Brenna would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of O'Shaughnessy to the teaching of Brenna would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such segmenting the historical user communication data into time blocks as part of the normalization of historical communication data. Further, applying segmenting the historical user communication data into time blocks as part of the normalization of historical communication data to Brenna would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient and accurate communication mode recommendations. By evaluating the communication history in blocks of time surrounding the current time, the combined system can more accurately determine communication preferences that may be time dependent. By looking at the communication mode preferences over blocks of time, the combined system can more accurately track and consider scheduling and behavior patterns related to communication mode preferences, resulting in more accurate rankings and suggestions to users.
The combination of Brenna and O'Shaughnessy still does not explicitly teach analyzing both the normalized participating user information and the user preference information to generate the ranking information of recommended communication modes by applying a machine learning model. Karp teaches:
using a data service module, analyzing the normalized participating user information and the user preference information and generating ranking information comprising recommended modes of communication for the participating user based on the analysis, wherein the generating of the ranking information comprises applying a machine-learning model trained on the normalized participating user information and the user preference information, the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps (see Col. 11 lines 40-45 “the system may select a messaging channel based on explicit user preference, implicit user preference, type of information system is providing (e.g., a optimal channel/medium to handle a specific intent token), machine learning predictions, or combinations thereof” and Col. 42 lines 11-18 “the dialogue management system 122 may select one or more particular messaging channels to send the message based on stored implicit preferences, explicit preferences, the type of information that the dialogue management system 122 is providing (e.g., based on an optimal channel for the specific intent), machine learning prediction of the optimal channel, or combinations thereof”, Col. 35 lines 8-11, and Col. 47 lines 41-62 for applying a machine learning model on implicit and explicit user communication preferences to determine a messaging mode. In combination with Brenna and O'Shaughnessy, the explicit and normalized implicit communication preferences of the receiving user are applied to machine learning models to predict the ranked methods. Examiner again notes here that the limitations of “trained on the normalized participating user information and the user preference information” and “the machine- learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight as they are not positively recited in the claim)
One of ordinary skill in the art would have recognized that applying the known technique of using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences of Karp to the combination of combination of Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination of Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences. Further, applying using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences to the combination of Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate analysis of the explicit and inferred communication preferences. Specifically, one of ordinary skill in the art would have recognized that use of machine learning in the combination of Brenna and O'Shaughnessy would provide the resulting combination with greater ability to recognize patterns in users’ communication interactions and stated preferences. This increased pattern recognition capability would allow for more accurate communication mode recommendations to be provided by the combined system.
Regarding claim 3, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above (Examiner notes that claim 3 is being interpreted as depending from claim 1 for the purposes of examination, see 35 U.S.C. 112(d) section above for details). Brenna further teaches:
wherein the modes of communication comprise one or more of: phone calls, chat, email, text, electronic meeting, electronic collaboration, social media, video, or augmented reality (see [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114")
Regarding claim 5, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. Brenna further teaches:
wherein the user preference information comprises a recommended mode of communication based on one or more of: an identifier of the initiating user, an identifier of the participating user, a day, a time of day, a location of the participating user, a sentiment of a user, a location of the initiating user, or a topic (see [0038] "Configurable communication mode preferences 204 allow a user to set a preferred communication mode (e.g., voice, chat email) for different types of contacts (e.g., close contacts, new contacts, org-external contacts, org-internal contacts, all contacts, or “other”). Other implementations may allow the user to specify an ordered list of preferred communication modes, either to use as a default across all contacts or for use in association with certain types of contacts (e.g., “ContactType” as shown)" for the user preference information being based on a type of participating user)
Regarding claim 6, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. Brenna further teaches:
wherein the step of sending the one or more recommended modes of communication comprises sending two or more ranked recommended modes of communication (see [0079] "A presentation operation 610 presents, on a user interface of the communication application, the inferred communication preference stored within a select one of the populated contact cards. For example, the inferred communication for a contact card of a first user is presented to a second user when the second user hovers over or clicks on the contact card of the first user" and Fig. 5 and [0073] "When John interacts with UI content corresponding to Paul's contact card 514 (e.g., a graphic including Paul's name, image, avatar), the contact card 514 is displayed. In this case, Paul's communication preferences are presented in a text box 516 that reads “Preferred communication method: Email, Chat, Voice calls, Video Meetings,” and the order is indicative of a preference from most preferred to least preferred")
Regarding claim 7, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. Brenna further teaches:
wherein the data service module determines a ranked recommended mode of communication based on one or more of: an identifier of the initiating user, a day, a time of day, a location of the participating user, a location of the initiating user, a topic, an identifier of the participating user, preference of the initiating user, or preference of the participating user (see [0051] "the inference extractor 308 may determine that John has a preference for voice chatting with Paul—either as an individual and/or with all contacts having a characteristic in common with Paul. Notably, Paul is one of John's “close contacts” which is, for example, a setting designated either manually by John or automatically by the system based on the frequency and/or volume of communications between John and Paul. If an analysis of the people graph 310 reveals that “voice” is the dominant communication mode for a threshold number (e.g., 80% or more) of the communications that John initiates with his close contacts, “voice” may be set as a communication preference for the “close contacts” grouping. Similar preferences may likewise be inferred with respect to other groups of contacts having some recognizable commonality. If, for example, the inference extractor 308 detects that John initiates a threshold number (e.g., 80%) of communications with “new contacts” by email, the inference extractor 308 may infers a communication preference for email communications with “new contacts” of John (e.g., users that have not previously communicated electronically with John)" for determining a ranked recommended mode of communication based on the identifier of the participating user and the preference of the initiating user)
Regarding claim 10, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. While Brenna teaches receiving both user preference information and ranked implicit preference information and a user profile datastore as a recommendation engine as discussed above, the combination of Brenna and O'Shaughnessy does not explicitly teach comparing the user preference and implied preference information to generate the recommended modes of communication. Karp further teaches:
further comprising using a recommendation engine to compare the user preference information and the ranking information to generate the one or more recommended modes of communication (see Col. 42 lines 42-45 “the dialogue management system 122 may receive the explicit preference input data from the customer representative device 150. In some embodiment's the explicit preference overrides any implicit preference or type of information to be provided considerations. Thus, if the user has an explicit preference to communicate via a mobile application messaging and the user has an implicit preference to communicate over SMS messaging, the dialogue management system 122 would select the communication channel that corresponds to the user's explicit preference” for comparing a received explicit communication mode preference and implicit communication mode preference and choosing the explicitly stated preference. In combination with Brenna, the system would compare the explicit and implicit preferences and, if they were to conflict, choose the explicit preference)
One of ordinary skill in the art would have recognized that applying the known technique of comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences of Karp to the combination Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences. Further, applying comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences to the combination Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow for a more responsive system. Brenna considers that relying on trend data alone may not always obtain the best results in [0064] “reliance on exclusively long-term trend data may in some cases be undesirable since long-term preferences are not necessarily the best prediction of current preferences, which may vary based on current events. A hybrid approach, as described above, may be offer a convenient blend of reliability and flexibility”. By considering the explicitly stated communication preferences of the user, and controlling the combined system can be more responsive to users’ current needs than the combination Brenna and O'Shaughnessy by letting the explicit preferences control when there is a discrepancy between stated and revealed preferences.
Regarding claim 11, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. Brenna further teaches:
further comprising, using an initiating user device, initiating a communication between the initiating user device and a participating user device (see Fig. 4 and [0069]-[0070] "When Sarah opens a chat window 402 within the communication application to begin corresponding with John (a person she has not previously corresponded with), communication preference information from John's contact card is presented within the chat window 402 on Sarah's display. Specifically, the chat window 402 displays the message “John prefers to be contacted by chat and his communication style is ‘No Hello.’” In this example, John's contact card information indicates a preference to be contacted by “Chat” (rather than email or phone)...Because John's communication preferences are presented to Sarah automatically at the time that Sarah is interacting with John's contact card, Sarah can tailor aspects of her outgoing communication to comply with John's preferences, thereby ensuring that her correspondence is less burdensome to John" for Sarah sending a chat message from her initiating device to John's participant device)
Regarding claim 13, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. The combination of Brenna and O'Shaughnessy does not explicitly teach weighting user preference information such that the user preference information is emphasized over the normalized participating user information when generating the recommended modes of communication. However, Karp further teaches:
wherein the user preference information is weighted such that the user preference information is emphasized over the normalized participating user information to generate the one or more recommended modes of communication (see Col. 42 lines 42-45 “the dialogue management system 122 may receive the explicit preference input data from the customer representative device 150. In some embodiment's the explicit preference overrides any implicit preference or type of information to be provided considerations. Thus, if the user has an explicit preference to communicate via a mobile application messaging and the user has an implicit preference to communicate over SMS messaging, the dialogue management system 122 would select the communication channel that corresponds to the user's explicit preference” for comparing a received explicit communication mode preference and implicit communication mode preference and choosing the explicitly stated preference. In combination with Brenna, the system would compare the explicit and implicit preferences and, if they were to conflict, choose the explicit preference)
One of ordinary skill in the art would have recognized that applying the known technique of comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences of Karp to the combination Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences. Further, applying comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences to the combination Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow for a more responsive system. Brenna considers that relying on trend data alone may not always obtain the best results in [0064] “reliance on exclusively long-term trend data may in some cases be undesirable since long-term preferences are not necessarily the best prediction of current preferences, which may vary based on current events. A hybrid approach, as described above, may be offer a convenient blend of reliability and flexibility”. By considering the explicitly stated communication preferences of the user, and controlling the combined system can be more responsive to users’ current needs than the combination Brenna and O'Shaughnessy by letting the explicit preferences control when there is a discrepancy between stated and revealed preferences.
Regarding claim 15, Brenna teaches:
An electronic communication system comprising (see Fig. 1 and [0018] "FIG. 1 illustrates an example communication system 100 that infers user communication preferences and exposes those preferences to other users interacting with a communication application" for overall system)
a data collection and normalization engine configured to receive historical participant user communication information from two or more modes of communication and to normalize the historical participant user communication information (see [0075] "The operations 600 includes a tracking operation 602 that tracks communication activity of a first user conducted in association with a first communication account to a communication application (e.g., emails, chats, calls, meetings)" and [0028]-[0033] for the tracking of the sender, recipient, and modes of each communication. See Fig. 3 and [0053]-[0058] for the communication tracker collecting data on the content of the messages. See [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114" for normalizing the historical communication into counter data tallying the number of communications made using the communication tracker, which Examiner is interpreting as a data collection and normalization engine)
a database comprising the normalized participating user information and participant user preference information, wherein the participant user preference information comprises ranked recommended modes of communication (see [0024] "each user profile within the user profile datastore 112 includes communication preference, such as information relating to a user's preferred mode of communication and/or the specific content preferences. Updates to profile data within the user profile datastore 112 are affected by “pushing” profile data collected on user devices to the user profile datastore. For example, one or more of the communication application(s) 104 on the personal device 102 may update local user profile data 116 for John, such as responsive to alterations to profile settings that John manually configures or responsive to preference inferences extracted by the communication system 100" and [0067] "updated information within the user profile communication preferences 312 may be transmitted to a global user profile datastore (e.g., the user profile datastore 112 of FIG. 1)" for a database of the participant user preference information. See [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient)" and [0045] "The system 300 includes a communication tracker 306 and an inference extractor 308 that perform at least some functions the same or similar to like-named components described with respect to FIG. 1" and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for storing the normalized information and participant preference information on an application server. See [0038] "Configurable communication mode preferences 204 allow a user to set a preferred communication mode (e.g., voice, chat email) for different types of contacts (e.g., close contacts, new contacts, org-external contacts, org-internal contacts, all contacts, or “other”). Other implementations may allow the user to specify an ordered list of preferred communication modes, either to use as a default across all contacts or for use in association with certain types of contacts (e.g., “ContactType” as shown)" for an ordered list of preferred communication modes)
a data service module configured to receive and analyze the normalized participating user information (see inference extractor 118 and 308 for the data service module. See [0051]-[0052] for the inference extractor accessing counts of communication modes and determining communication mode preferences. See [0030] "From this tracked information, the inference extractor 118 may infer that John's communication preferences for correspondence with Paul are, in descending order of preference: chat, voice, and email. Notably, this trend may be true across most or all of John's contacts. If so, a communication mode preference may be updated within the local user profile data 116 to indicate that, as a default, John's mode of communication preferences are, in descending order: chat, voice, and email regardless of sender identity" for accessing normalized communication information to generating ranking of recommended modes of communication)
and a recommendation engine to receive the ranking of recommended modes of communication information and the participant user preference information and to provide a recommended mode of communication to an initiating user device (see user profile datastore 112 for the recommendation engine and [0025] "When the local user profile data 116 is updated on John's device (the personal device 102), this information—including updated communication preferences—is pushed to the user profile datastore 112 and is ultimately downloaded to the devices 122, 124 and made accessible to Paul and Sarah through their respective communication accounts" and [0027] "the local user profile data 116 includes communication preferences of the associated account owner (John). Updates to these communication preferences may be manually specified the account owner (e.g., John), such as within configurable settings of a corresponding one of the communication applications 104 and/or inferred by software components of the communication system 100" for the receipt of inferred preference rankings and manually entered preferences and providing recommended communication methods to other users)
While Brenna teaches normalizing historical communication data by determining counts of communication modes and participants in a communication session as discussed above, Brenna does not explicitly teach normalizing the historical communication information by segmenting the communication information into blocks based on the type of the historical communication information. Brenna does not explicitly teach analyzing the normalized participating user data to generate a ranking of communication modes by using a machine learning model. O'Shaughnessy teaches:
normalize the historical participant user communication information by segmenting the historical participant user communication information into blocks of data based on a type of the historical participant user communication information to provide normalized participating user information (see Fig. 6 and [0090]-[0092] “At step 600, an initial mode is selected and at step 601 the call history data is analyzed for that particular selected mode to determine within a window of .+-.N minutes from the current time how many calls were made using the mode, covering a number of days specified by variable D…At step 602, this count is divided by D to give a short term average number of calls per day…A simple process finds a count for a larger period of H days and a long term average number of calls per day is calculated” and [0097] “Step 611 then determines whether there are further modes to be processed and if so the next mode is selected at step 613 and the process recommences from 601” for segmenting the communication data into blocks within N minutes of the current time over a plurality of days. See [0038] for different communication types including landline telephone, mobile telephone, push to talk telephone communication, etc. In combination with Brenna, the communication information is segmented into the time blocks as part of the normalization. See Applicant’s disclosure [0018] for grouping data into blocks of time as normalizing the data)
One of ordinary skill in the art would have recognized that applying the known technique of segmenting the historical user communication data into time blocks as part of the normalization of historical communication data of O'Shaughnessy to the system of Brenna would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of O'Shaughnessy to the teaching of Brenna would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such segmenting the historical user communication data into time blocks as part of the normalization of historical communication data. Further, applying segmenting the historical user communication data into time blocks as part of the normalization of historical communication data to Brenna would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient and accurate communication mode recommendations. By evaluating the communication history in blocks of time surrounding the current time, the combined system can more accurately determine communication preferences that may be time dependent. By looking at the communication mode preferences over blocks of time, the combined system can more accurately track and consider scheduling and behavior patterns related to communication mode preferences, resulting in more accurate rankings and suggestions to users.
The combination of Brenna and O'Shaughnessy does not explicitly teach analyzing the normalized participating user data to generate a ranking of communication modes and types by using a machine learning model. Karp teaches:
a data service module configured to receive and analyze the normalized participating user information using a machine-learning model trained on the normalized participating user information and the participant user preference information, the machine-learning model being iteratively updated based on subsequently received historical participant user communication information (see Col. 11 lines 40-45 “the system may select a messaging channel based on explicit user preference, implicit user preference, type of information system is providing (e.g., a optimal channel/medium to handle a specific intent token), machine learning predictions, or combinations thereof” and Col. 42 lines 11-18 “the dialogue management system 122 may select one or more particular messaging channels to send the message based on stored implicit preferences, explicit preferences, the type of information that the dialogue management system 122 is providing (e.g., based on an optimal channel for the specific intent), machine learning prediction of the optimal channel, or combinations thereof”, Col. 35 lines 8-11, and Col. 47 lines 41-62 for applying a machine learning model on implicit and explicit user communication preferences to determine a messaging mode. In combination with Brenna and O'Shaughnessy, the explicit and normalized implicit communication preferences of the receiving user are applied to machine learning models to predict the ranked methods. Examiner again notes here that the limitations of “trained on the normalized participating user information and the participant user preference information” and “the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight as they are not positively recited in the claim)
One of ordinary skill in the art would have recognized that applying the known technique of using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences of Karp to the combination of combination of Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination of Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences. Further, applying using a machine learning model to analyze stated communication mode preferences and implicit communication mode preferences to the combination of Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate analysis of the explicit and inferred communication preferences. Specifically, one of ordinary skill in the art would have recognized that use of machine learning in the combination of Brenna and O'Shaughnessy would provide the resulting combination with greater ability to recognize patterns in users’ communication interactions and stated preferences. This increased pattern recognition capability would allow for more accurate communication mode recommendations to be provided by the combined system.
Regarding claim 16, the combination of Brenna, O'Shaughnessy and Karp teaches all of the limitations of claim 15 above. Brenna further teaches:
wherein the data collection and normalization engine resides on a communication server (see [0033] "Various aspects of the communication tracker 114 and the inference extractor 118 may be cloud-based", [0045] “The system 300 includes a communication tracker 306 and an inference extractor 308 that perform at least some functions the same or similar to like-named components described with respect to FIG. 1”, and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for the communication tracker being on a server)
Regarding claim 17, the combination of Brenna, O'Shaughnessy and Karp teaches all of the limitations of claim 15 above. Brenna further teaches:
wherein the data service module resides on a communication server (see [0033] "Various aspects of the communication tracker 114 and the inference extractor 118 may be cloud-based")
Regarding claim 18, the combination of Brenna, O'Shaughnessy and Karp teaches all of the limitations of claim 15 above. Brenna further teaches:
wherein the recommendation engine resides on a communication server (see [0023] "The user profile datastore 112 is, for example, a cloud-based profile database system that includes profile data for various users")
Claims 8-9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Brenna in view of O'Shaughnessy, Karp, and Stafford et al. (U.S. Pre-Grant Publication No. 2010/0318486, hereafter known as Stafford).
Regarding claim 8, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. While Brenna teaches viewing communication data from previous time periods in [0059] and determining if any temporary changes need to be made to the inferred preferences in [0060], the combination of Brenna, O'Shaughnessy, and Karp does not explicitly teach sending a recommended time for communication. Stafford teaches:
further comprising sending a recommended time for communication (see Fig. 5B indicating that the message will be sent in 5 seconds and [0031] "The user's wireless communications device instant messaging screen 510 may display an instant message received from the proactive communications device that indicates which client the automatic proactive communicator is about to attempt to communicate an instant message with" and [0036] for determining the best time to contact a particular participating user and displaying that time on the initiating user's device)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the determination and sending of a recommended time of communication of Stafford into the combination of Brenna, O'Shaughnessy, and Karp. As Stafford states in [0036] “Related to the present invention, using a body of data of past calls, the likelihood of a person being available at particular time may be updated based on information received from future attempts at contacting a particular contact. For example, if a contact has only been available before 9 a.m. once, each additional attempt at communication with the contact prior to 9 a.m. would diminish the chances of the contact being available before 9 a.m. The automatic proactive communicator would eventually have enough data to determine that the contact should not be called before 9 a.m., to allow for the most efficient use of the user's time. Data may be compiled not only on the contact, but also on the availability of the user, as well. For example, if the user frequently aborts communication attempts between a set period of time (for example between 4 and 6 pm), the algorithms may determine that it would be best to not attempt communications during these times”. In short, the incorporation of determining and recommending a particular time for communication results in a more efficient use of both the recipient’s and sender’s time, as communications are suggested at times when both the sender and the recipient are historically likely to be available and willing to communicate and not during times when they are unlikely to be available or willing to respond. Users’ time is therefore not wasted on viewing and declining recommendations that the user is unlikely to accept.
Regarding claim 9, the combination of Brenna, O'Shaughnessy, Karp, and Stafford teaches all of the limitations of claim 8 above. While Brenna teaches users initiating the communication through the recommended modes of communication, the combination of Brenna, O'Shaughnessy, and Karp does not explicitly teach automatically connecting initiating and participant user devices at a recommended time. However, Stafford further teaches:
further comprising a step of automatically connecting an initiating user device to a communication with a participant user device at a recommended time (see Fig. 5B indicating that the message will be sent in 5 seconds and [0031] "The user's wireless communications device instant messaging screen 510 may display an instant message received from the proactive communications device that indicates which client the automatic proactive communicator is about to attempt to communicate an instant message with...the user may not select anything and wait for an automatic communication of the instant message" for the automatic IM connection at the recommended time)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the automatic connection of a sender and recipient device at a recommended communication time of Stafford into the combination of Brenna, O'Shaughnessy, Karp, and Stafford. As Stafford states in [0032], “By automatically initiating a telephone call to the user, the automatic proactive communicator allows the user to effortlessly keep in touch with the contacts without any additional effort on the user's part. If the user has time and wishes to talk to the contact, the user simply has to wait and will be connected to the contact”. Therefore, by automatically initiating communications at the recommended times, users can effortlessly keep in touch with contacts simply by allowing the automatic calling/contacting of a contact to proceed at the recommended time when a contact is likely to accept/reply to communications.
Regarding claim 12, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 11 above. While Brenna teaches ranking communication modes in order of implied preference and users manually connecting via these preferred methods as discussed above, the combination of Brenna, O'Shaughnessy, and Karp does not explicitly teach automatically connecting the initiating and participant user devices using a highest ranking modes of communication. Stafford teaches:
further comprising automatically connecting the initiating user device to a communication with the participating user device using a highest ranked recommended mode of communication (see [0038] "if information for the contact is known for the telephone number, e-mail address or instant messenger, based upon learned experience as determined by the logic module or other means, one of the initiate modules 608-612 may be called to initiate the communication" and see [0030]-[0032] for the automatic initiation of the contact between the initiating and participating devices. In combination with the ranked modes of Brenna, the one mode selected based upon learned experience of Stafford [0038] would be the highest ranked mode)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the automatic connection of a sender and recipient device via a recommended communication mode of Stafford into the combination of Brenna, O'Shaughnessy, and Karp. As Stafford states in [0032], “By automatically initiating a telephone call to the user, the automatic proactive communicator allows the user to effortlessly keep in touch with the contacts without any additional effort on the user's part. If the user has time and wishes to talk to the contact, the user simply has to wait and will be connected to the contact”. Therefore, by automatically initiating communications at the recommended times, users can effortlessly keep in touch with contacts simply by allowing the automatic calling/contacting of a contact to proceed via the highest preferred mode of communication of the contact.
Claims 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Brenna in view of O'Shaughnessy, Karp, and Wilkins et al. (U.S. Pre-Grant Publication No. 2008/0205655, hereafter known as Wilkins).
Regarding claim 14, the combination of Brenna, O'Shaughnessy, and Karp teaches all of the limitations of claim 1 above. Brenna further teaches sending a recommended mode of communication as discussed in the rejection of claim 1 above. While O'Shaughnessy teaches presenting an ordered list of modes and types of communication to an initiating user device, the modes and types are ranked together, so the combination of Brenna, O'Shaughnessy, and Karp does not explicitly teach the sending of a recommended mode of communication AND a recommended type of communication. Wilkins teaches:
wherein the step of sending one or more recommended modes of communication comprises sending a recommended mode and a recommended type of communication (see [0185] "A request by User B's network device for User A's serial number and contact record results in the dissemination User A's contact information to User B's network device along with the preferred methods of contacting User A. The network device is adapted with a preferred method manager 536 (FIG. 11b). When User B views User A's contact record, User B's network device may present the preferred method for contacting User A" for sending recommended contact information, and [0186] "when User B attempts to contact User A by a text-based method (e.g., email, SMS, or IM), the network device retrieves the preferred method, and the address or network identifier for the preferred contact method from the contact record. User B may then send the text message to User A via that method" and [0187] "when User B attempts to contact User A by voice-based methods (e.g., telephone, wireless phone, or VOIP) the network device attempts to connect via preferred methods in an ordinal manner (e.g., via VOIP first, via PSTN second, via cellular phone third" for sending the preferred type of communication. Particularly, sending a text or voice-based message is a preferred communication mode, then automatically receiving information for the preferred type of text- or voice-based communication (telephone, wireless phone, etc.) so the initiating device can start the communication)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the ranking and recommendation of communication types as well as communication modes as taught by Wilkins in the combination of Brenna, O'Shaughnessy, and Karp, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Specifically, Brenna already shows the capability to count the number of times communications are made within individual communication apps 106-110 and ranking these communication modes. One of ordinary skill in the art viewing Brenna and Wilkins would recognize that the counting and ranking method of Brenna can be further applied to result in a ranking of communication types as well as modes as taught in Wilkins. The incorporation of communication types has predictable results and would have been within the ability of one of ordinary skill in the art, as the counting method of Brenna would merely be applied to communication types and modes when generating rankings.
Regarding claim 20, Brenna teaches:
An electronic communication method comprising (see Fig. 6 and [0075]-[0079] for overall method of electronic communication)
collecting communication information from two or more modes of communication for a participating user, the communication information comprising a plurality of modes of communication (see [0075] "The operations 600 includes a tracking operation 602 that tracks communication activity of a first user conducted in association with a first communication account to a communication application (e.g., emails, chats, calls, meetings)" and [0028]-[0033] for the tracking of the sender, recipient, and modes of each communication, particularly [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114". See Fig. 3 and [0053]-[0058] for the communication tracker collecting data on the content of the messages)
using a data collection and normalization engine, normalizing the communication information for the participating user (see [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient). For example, a table 120 indicates that John has called Paul 12 times, text-chatted with Paul 18 different times, and emailed Paul three times since the start of corresponding interval represented by the illustrated table within the communication tracker 114" for normalizing the historical communication into counter data tallying the number of communications made using the communication tracker, which Examiner is interpreting as a data collection and normalization engine)
collecting user preference information for the participating user, wherein the user preference information comprises ranked recommended modes of communication (see Fig. 2 and [0038] "Configurable communication mode preferences 204 allow a user to set a preferred communication mode (e.g., voice, chat email) for different types of contacts (e.g., close contacts, new contacts, org-external contacts, org-internal contacts, all contacts, or “other”). Other implementations may allow the user to specify an ordered list of preferred communication modes, either to use as a default across all contacts or for use in association with certain types of contacts (e.g., “ContactType” as shown)" for an ordered list of communication mode preferences from the user)
storing the normalized user information in a database (see [0030] "the communication tracker 114 keeps a counter in association with each different mode of communication utilized within a same participant group (e.g., sender/recipient)" and [0045] "The system 300 includes a communication tracker 306 and an inference extractor 308 that perform at least some functions the same or similar to like-named components described with respect to FIG. 1" and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for storing the normalized information on application servers)
storing the user preference information in the database (see [0024] "each user profile within the user profile datastore 112 includes communication preference, such as information relating to a user's preferred mode of communication and/or the specific content preferences. Updates to profile data within the user profile datastore 112 are affected by “pushing” profile data collected on user devices to the user profile datastore. For example, one or more of the communication application(s) 104 on the personal device 102 may update local user profile data 116 for John, such as responsive to alterations to profile settings that John manually configures or responsive to preference inferences extracted by the communication system 100" and [0067] "updated information within the user profile communication preferences 312 may be transmitted to a global user profile datastore (e.g., the user profile datastore 112 of FIG. 1)" and [0065] "some of the illustrated software components are executed remotely, such as at one or more a cloud-based application servers" for storing the user preference information on the application servers)
using a data service module, analyzing the normalized participating user information (see inference extractor 118 and 308 for the data service module. See [0051]-[0052] for the inference extractor accessing counts of communication modes and determining communication mode preferences. See [0030] "From this tracked information, the inference extractor 118 may infer that John's communication preferences for correspondence with Paul are, in descending order of preference: chat, voice, and email. Notably, this trend may be true across most or all of John's contacts. If so, a communication mode preference may be updated within the local user profile data 116 to indicate that, as a default, John's mode of communication preferences are, in descending order: chat, voice, and email regardless of sender identity" for accessing normalized communication information to generating ranking of recommended modes of communication)
and sending a recommended mode (see [0079] "A presentation operation 610 presents, on a user interface of the communication application, the inferred communication preference stored within a select one of the populated contact cards. For example, the inferred communication for a contact card of a first user is presented to a second user when the second user hovers over or clicks on the contact card of the first user" and Fig. 5 and [0073] "When John interacts with UI content corresponding to Paul's contact card 514 (e.g., a graphic including Paul's name, image, avatar), the contact card 514 is displayed. In this case, Paul's communication preferences are presented in a text box 516 that reads “Preferred communication method: Email, Chat, Voice calls, Video Meetings,” and the order is indicative of a preference from most preferred to least preferred")
As discussed above regarding claim 10, Brenna does not explicitly teach the comparison of the ranked modes and types of communication with the user communication preference information. Additionally, Brenna also does not explicitly teach the collecting of information on the type of communication for each mode of communication used, generating a ranking information of the type of communication used, and sending the recommended type as well as mode of communication to the initiating user device. Finally, Brenna does not explicitly teach analyzing the normalized participating user data to generate a ranking of communication modes and types by using a machine learning model. However, O'Shaughnessy teaches:
collecting communication information from two or more modes of communication for a participating user, the communication information comprising a plurality of modes of communication and one or more types of communication for each mode (see [0060] “step 503 follows in which the mode comparator module 118 determines whether historical data is available from the historical data module 117. If available, the relevant call history is retrieved from the communications history database 120. At step 504 the mode comparator module 118 calculates for each of the communication modes a respective call history score” and [0032]. See [0038] for multiple modes and types (telephone communication via mobile telephone, push-to-talk, etc.) of communication being collected)
using a data collection and normalization engine, normalizing the communication information for the participating user by segmenting the historical communication information into blocks of data based on a type of the historical communication information to provide normalized participating user information (see Fig. 6 and [0090]-[0092] “At step 600, an initial mode is selected and at step 601 the call history data is analyzed for that particular selected mode to determine within a window of .+-.N minutes from the current time how many calls were made using the mode, covering a number of days specified by variable D…At step 602, this count is divided by D to give a short term average number of calls per day…A simple process finds a count for a larger period of H days and a long term average number of calls per day is calculated” and [0097] “Step 611 then determines whether there are further modes to be processed and if so the next mode is selected at step 613 and the process recommences from 601” for segmenting the communication data into blocks within N minutes of the current time over a plurality of days. See [0038] for different communication types including landline telephone, mobile telephone, push to talk telephone communication, etc. In combination with Brenna, the communication information is segmented into the time blocks as part of the normalization. See Applicant’s disclosure [0018] for grouping data into blocks of time as normalizing the data)
using a data service module, analyzing the normalized participating user information (see Fig. 5 and [0060]-[0063] and [0077]-[0080] for the generation of an ordered list of modes and types of communication based on analysis of the normalized historical communication information of the participating user)
One of ordinary skill in the art would have recognized that applying the known technique of segmenting the historical user communication data into time blocks as part of the normalization of historical communication data and analyzing the normalized data to generate ranking information on modes and types of communications of O'Shaughnessy to the system of Brenna would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of O'Shaughnessy to the teaching of Brenna would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such segmenting the historical user communication data into time blocks as part of the normalization of historical communication data and analyzing the normalized data to generate ranking information on modes and types of communications. Further, applying segmenting the historical user communication data into time blocks as part of the normalization of historical communication data and analyzing the normalized data to generate ranking information on modes and types of communications to Brenna would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient and accurate communication mode recommendations. By evaluating the communication history in blocks of time surrounding the current time, the combined system can more accurately determine communication preferences that may be time dependent. By looking at the communication mode preferences over blocks of time, the combined system can more accurately track and consider scheduling and behavior patterns related to communication mode preferences, resulting in more accurate rankings and suggestions to users.
While Brenna teaches receiving both user preference information and ranked implicit preference information and a user profile datastore as a recommendation engine as discussed above, the combination of Brenna and O'Shaughnessy does not explicitly teach using a machine learning model to generate the rankings of communication modes and types, and comparing the user preference and implied preference information to generate the recommended modes of communication. The combination of Brenna and O'Shaughnessy also does not explicitly teach sending the recommended type as well as mode of communication to the initiating user device. Karp teaches:
analyzing the normalized participating user information using a machine-learning model trained on the normalized participating user information and the participant user preference information, the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps and generating ranking information of recommended modes of communication for the participating user and ranking information of recommended types of communication for the participating user based on the analysis (see Col. 11 lines 40-45 “the system may select a messaging channel based on explicit user preference, implicit user preference, type of information system is providing (e.g., a optimal channel/medium to handle a specific intent token), machine learning predictions, or combinations thereof” and Col. 42 lines 11-18 “the dialogue management system 122 may select one or more particular messaging channels to send the message based on stored implicit preferences, explicit preferences, the type of information that the dialogue management system 122 is providing (e.g., based on an optimal channel for the specific intent), machine learning prediction of the optimal channel, or combinations thereof”, Col. 35 lines 8-11, and Col. 47 lines 41-62 for applying a machine learning model on implicit and explicit user communication preferences to determine a messaging mode. In combination with Brenna and O'Shaughnessy, the explicit and normalized implicit communication preferences of the receiving user are applied to machine learning models to predict the ranked methods. Examiner again notes here that the limitations of “trained on the normalized participating user information and the participant user preference information” and “the machine-learning model being iteratively updated based on subsequent historical communication information collected during repeated executions of the collecting and normalizing steps” are not being given patentable weight as they are not positively recited in the claim)
comparing the ranking information of recommended modes of communication and the ranking information of recommended types of communication for the participating user to the preference information (see Col. 42 lines 42-45 “the dialogue management system 122 may receive the explicit preference input data from the customer representative device 150. In some embodiment's the explicit preference overrides any implicit preference or type of information to be provided considerations. Thus, if the user has an explicit preference to communicate via a mobile application messaging and the user has an implicit preference to communicate over SMS messaging, the dialogue management system 122 would select the communication channel that corresponds to the user's explicit preference” for comparing a received explicit communication mode preference and implicit communication mode preference and choosing the explicitly stated preference. In combination with Brenna, the system would compare the explicit and implicit preferences and, if they were to conflict, choose the explicit preference)
One of ordinary skill in the art would have recognized that applying the known technique of using a machine learning model to analyze stated communication mode preferences and implicit communication mode/type preferences of Karp to the combination of combination of Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination of Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such using a machine learning model to analyze stated communication mode/type preferences and implicit communication mode preferences. Further, applying using a machine learning model to analyze stated communication mode/type preferences and implicit communication mode preferences to the combination of Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate analysis of the explicit and inferred communication preferences. Specifically, one of ordinary skill in the art would have recognized that use of machine learning in the combination of Brenna and O'Shaughnessy would provide the resulting combination with greater ability to recognize patterns in users’ communication interactions and stated preferences. This increased pattern recognition capability would allow for more accurate communication mode recommendations to be provided by the combined system.
Regarding the comparison of ranked inferred preferences obtained by the machine learning model and stated user preferences, one of ordinary skill in the art would have recognized that applying the known technique of comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences of Karp to the combination Brenna and O'Shaughnessy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Karp to the teaching of the combination Brenna and O'Shaughnessy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences. Further, applying comparing explicitly stated communication preferences with implicit preferences and choosing explicit preferences when there is a conflict between preferences to the combination Brenna and O'Shaughnessy would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow for a more responsive system. Brenna considers that relying on trend data alone may not always obtain the best results in [0064] “reliance on exclusively long-term trend data may in some cases be undesirable since long-term preferences are not necessarily the best prediction of current preferences, which may vary based on current events. A hybrid approach, as described above, may be offer a convenient blend of reliability and flexibility”. By considering the explicitly stated communication preferences of the user, and controlling the combined system can be more responsive to users’ current needs than the combination Brenna and O'Shaughnessy by letting the explicit preferences control when there is a discrepancy between stated and revealed preferences.
While O'Shaughnessy teaches presenting an ordered list of modes and types of communication to an initiating user device, the modes and types are ranked together, so the combination of Brenna, O'Shaughnessy, and Karp does not explicitly teach the sending of a recommended mode of communication AND a recommended type of communication. However, Wilkins teaches:
and sending a recommended mode and type of communication to an initiating user device (see [0185] "A request by User B's network device for User A's serial number and contact record results in the dissemination User A's contact information to User B's network device along with the preferred methods of contacting User A. The network device is adapted with a preferred method manager 536 (FIG. 11b). When User B views User A's contact record, User B's network device may present the preferred method for contacting User A" for sending recommended contact information, and [0186] "when User B attempts to contact User A by a text-based method (e.g., email, SMS, or IM), the network device retrieves the preferred method, and the address or network identifier for the preferred contact method from the contact record. User B may then send the text message to User A via that method" and [0187] "when User B attempts to contact User A by voice-based methods (e.g., telephone, wireless phone, or VOIP) the network device attempts to connect via preferred methods in an ordinal manner (e.g., via VOIP first, via PSTN second, via cellular phone third" for sending the preferred type of communication. Particularly, sending a text or voice-based mode is a preferred communication mode, then automatically receiving information for the preferred type of text- or voice-based communication so the initiating device can start the communication)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the collection of information on, as well as the ranking and recommendation of, communication types and communication modes as taught by Wilkins in the combination of Brenna, O'Shaughnessy, and Karp, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Specifically, Brenna already shows the capability to count the number of times communications are made within individual communication apps 106-110 and ranking and recommending these communication modes. One of ordinary skill in the art viewing Brenna and Wilkins would recognize that the counting, ranking, and recommending method of Brenna can be further applied to result in a ranking of communication types as well as modes as taught in Wilkins. The incorporation of communication types has predictable results and would have been within the ability of one of ordinary skill in the art, as the counting method of Brenna would merely be applied to communication types and modes when generating rankings.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Brenna in view of O'Shaughnessy, Karp, and Chakraborty et al. (U.S. Pre-Grant Publication No. 2024/0179538, hereafter known as Chakraborty).
Regarding claim 19, the combination of Brenna, O'Shaughnessy and Karp teaches all of the limitations of claim 15 above. While Brenna teaches inferring content/style preferences of a user when receiving communications in at least [0053] and [0054] and Karp considers a sentiment analysis in Col. 27 lines 45-49, the combination of Brenna, O'Shaughnessy and Karp does not explicitly teach a sentiment analysis engine determining a sentiment of the communication of the participating user. However, Chakraborty teaches:
further comprising a sentiment analysis engine to determine a sentiment of the communication information for the participating user (see [0102] "data models are created using artificial intelligence models and predictive analytics. The data models are specific to communication tendencies of a user. The insights/capabilities may include the recommendation of a best communication platform based on the context of message and the type of message being shared...The communication mode management program 116 may understand user behavioral patterns from the communication history data, may define a user fingerprint to understand a mood of the user at a specific point in time based on the current conditions, and based on the mood may recommend a type of content and message content modifications for users trying to communicate with the first user" and [0067] "If a sender is sending a message to receiver, the communication mode management program 116 may understand a mood of the receiver by analyzing various factors such as word tones and vocabulary choice of messages and/or conversations of the receiver. Based on a sour mood of the receiver, the communication mode management program 116 may recommend a different wording for the message content and may send this proposal to the sender")
One of ordinary skill in the art would have recognized that applying the known technique of analyzing the mood of a recipient and suggestions alterations to the content of messages based on the determined mood of Chakraborty to the combination of Brenna, O'Shaughnessy and Karp would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chakraborty to the teaching of the combination of Brenna, O'Shaughnessy and Karp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such analyzing the mood of a recipient and suggestions alterations to the content of messages based on the determined mood. Further, applying analyzing the mood of a recipient and suggestions alterations to the content of messages based on the determined mood to the combination of Brenna, O'Shaughnessy and Karp would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow the communication sender to get the best reception from the recipient. On top of communicating in the recipient’s preferred mode, the combined system would also assist the sender in navigating the recipient’s mood. The combined system’s suggestion of message content based on the recipient’s mood would further allow communication between the users proceed more smoothly and tactfully.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yanes et al. (U.S. Patent No. 10,764,232) teaches determining a communication preference hierarchy for a recipient user based on various factors related to the recipient user including communication history
Sadr et al. (U.S. Pre-Grant Publication No. 2024/0202987) teaches inputting explicitly stated user preferences, learned preferences, and preference weighting into a machine learning model to make suggestions to a user
Kumar et al. (U.S. Pre-Grant Publication No. 2023/0135380) teaches inputting user preferences and previous interactions of a user into a machine learning model to determine recommended resources for a meeting
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/M.C.M./Examiner, Art Unit 3628
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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