Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Claim Objections
1. Claims 1, 7, 14 is objected to because of the following informalities: claims 1, 7, line 6 and claim 14, line 2 recite “(a’) train an algorithm,…” should be – train an algorithm… - . Appropriate correction is required.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Each of the independent claims 1, 7, and 14 recites steps that result in determining user temperament, predicting a ranking of the user temperament, testing and comparing the ranking of the user temperament predicted and indicating whether modifications to weights assigned are necessary to improve predictability of the ranking of the user temperament. The claims recite train an algorithm via machine learning but lack of detail in the claims as to the form of the machine learning (e.g., layers, nodes, etc. and what they do); and using a computing device and module to identify and determine a value and disposition of an object is merely applying the judicial exception using a generic computing component. All of the recited steps are processes that, under its broadest reasonable interpretation, cover the limitations under the organized human activity with paper and pen.
The claim features under its broadest reasonable interpretation, are certain methods of organizing human activity performed by generic computer components. For example, but for the “predicting” [human activity: forecasting, foretelling], “testing and comparing” [human behavior: challenging and collating], and “indicating” [human behavior: specifying, showing], in the context of this claim encompasses methods of organized human activity. If the claim limitations, under its broadest reasonable interpretation, covers fundamental economic practice, commercial or legal interaction or managing personal behavior or relationships or interactions between people but for the recitation of generic computer components, then it falls within the "system/method of organized human activity" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
"[A]fter determining that a claim is directed to a judicial exception, 'we then ask, [w]hat else is there in the claims before us?"' MPEP 2106.05 (emphasis in MPEP) citing Mayo, 566 U.S. at 78. "What is needed is an inventive concept in the non-abstract application realm." SAP Inc. v. lnvestPic, LLV, Appeal No. 2017-2081 (Fed. Cir. 2018). For step two, the examiner must "determine whether the claims do significantly more than simply describe [the] abstract method" and thus transform the abstract idea into patent-eligible subject matter. Ultramercial, Inc. v. Hutu, LLC, 772 F.3d 709 (Fed. Cir. 2014).
A primary consideration when determining whether a claim recites "significantly more" than abstract idea is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. See MPEP 2106.0S{d). "If the additional element (or combination of elements) is a specific limitation other than what is well- understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element {or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility." Id.
The Federal Circuit has held that "[w]hether something is well-understood, routine, and conventional to a skilled artisan at the time of the patent is a factual determination." Bahr, Robert (April 19, 2018). Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.) citing Berkheimer at 1369. "As set forth in MPEP 2106.05(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. This memo [] clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III [of the memo]." Berkheimer Memo at 3 (emphasis in memo).
Generally, "[i]f a patent uses generic computer components to implement an invention, it fails to recite an inventive concept under Alice step two." West View Research v. Audi, CAFC Appeal Nos. 2016-1947-51 (Fed. Cir. 04/19/2017) citing Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324-25 (Fed. Cir. 2016) (explaining that "generic computer components such as an 'interface,' 'network,' and 'database' ... do not satisfy the inventive concept requirement"; but see Bascom (finding that an inventive concept may be found in the non-conventional and non-generic arrangement of the generic computer components, i.e., the installation of a filtering tool at a specific location, remote from the end- users, with customizable filtering features specific to each end user).
In accordance with the above guidance, the examiner has searched the claim(s) to determine whether there are any "additional elements" in the claims that constitute "inventive concept," thereby rendering the claims eligible for patenting even if they are directed to an abstract idea. Alice, 134 S. Ct. 2347 (2014). Those "additional features" must be more than "well understood, routine, conventional activity." See Alice. To note, "under the Mayo/Alice framework, a claim directed to a newly discovered ... abstract idea [] cannot rely on the novelty of that discovery for the inventive concept necessary for patent eligibility." Genetic Techs. Ltd v. Merial LLC, 818 F.3d 1369, 1376 (Fed. Cir. 2016); Diamond v. Diehr, 450 U.S. 175, 188-89 (1981).
As an example, the Federal Circuit has indicated that "inventive concept" can be found where the claims indicate the technological steps that are undertaken to overcome the stated problem(s) identified in Applicant's originally-filed Specification. See Trading Techs. Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017); but see IV v. Erie Indemnity, No. 2016-1128 (Fed. Cir. March 7, 2017) ("The claims are not focused on how usage of the XML tags alters the database in a way that leads to an improvement in technology of computer databases, as in Enfish.") (emphasis in original) and IV. v. Capital One, Nos. 2016-1077 (Fed. Cir. March 7, 2017) ("Indeed, the claim language here provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it. Our law demands more. See Elec. Power Grp., 830 F.3d 1356 (Fed. Cir. 2016) (cautioning against claims 'so result focused, so functional, as to effectively cover any solution to an identified problem.')"). Furthermore, "[a]bstraction is avoided or overcome when a proposed new application or computer-implemented function is not simply the generalized use of a computer as a tool to conduct a known or obvious process, but instead is an improvement to the capability of the system as a whole." Trading Techs. Int'l, Inc. v. CQG, Inc., No. 2016-1616 (Fed. Cir. 2017) (emphasis added).
In the search for inventive concept, the Berkheimer Memo describes "an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A citation to one or more of the court decisions discussed in the MPEP as noting the well-understood, routine, conventional nature of the additional element(s).
A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).
A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s).
See Berkheimer Memo at 3-4.
Accordingly, the examiner refers to the following generically-recited computer elements with their associated functions (and associated factual finding(s)), which are considered, individually and in combination, to be routine, conventional, and well-understood:
“a system for determining user temperament comprising”,
“a method for determining and processing user temperament, the method comprising”
As set forth in MPEP § 2106.0S(d)(I), an examiner should conclude that an element (or combination of elements) represents well-understood, routine, conventional activity only when the examiner can readily conclude that the element(s) is widely prevalent or in common use in the relevant industry. The Berkhiemer memo clarifies that such a conclusion must be based upon a factual determination that is supported as discussed in section III the memo. As seen in paragraphs ([28, 99, 104, 111]) of the instant Specification and Symantec.. 838 F.3d at 1.321, 110 USPQ2d at. 1362, the elements are viewed to be well-understood, routine and conventional.
In sum, the Examiner finds that the claims "are directed to the use of conventional or generic technology in a nascent but well-known environment, without any claim that the invention reflects an inventive solution to any problem presented by combining the two." In re TLI Communications LLC, No. 2015-1372 (May 17, 2016). Similar to the claims in SAP v. lnvestPic, "[t]he claims here are ineligible because their innovation is an innovation in ineligible subject matter." Appeal No. 2017-2081 (Fed. Cir. 2018). In other words, "the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm." Id. Accordingly, when considered individually and in ordered combination, the examiner finds the claims to be directed to in-eligible subject matter.
Next, it is determined whether the claim integrates the judicial expectation into a practical application by identifying whether “any additional elements recited in the claim beyond the judicial exception(s)” and evaluate those elements to determine whether the integrate the judicial exception into a recognized practical application.
In this case, the additional elements do not integrate the judicial application into a practical application. The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field ; (ii) a "particular machine" to apply or use the judicial exception; (iii) a particular transformation of an article to a different thing or state; or (iv) any other meaningful limitation.
The additional elements beyond the judicial exception are at least one processor, a communication interface communicatively coupled to the at least on processor, and a memory device storing code, machine learning. A lack of detail in the claims as to the form of the machine learning (e.g., layers, nodes, etc. and what they do). Using a computing device and module to identify and determine a value and disposition of an object is merely applying the judicial exception using a generic computing component. Additionally, the claim identifies and determines a value and disposition of an object - the claim does not improve the functioning of the computing device, or other technology or field.
The claims do not recite specific limitations (alone or when considered as an ordered combination) that were not well understood, routine, and conventional. As set forth in the Specification, the disclosed subject matter can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
5. Claims 1, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Yeri et al. (2021/0097605) in view of Saa-Garriga et al. (2021/0065649).
As to claim 1, Yeri teaches a system for determining user temperament comprising: at least one processor; a communication interface communicatively coupled to the at least one processor and a memory device storing executable code ([0005, 0024, 0086]) that when executed causes the at least one processor to:
train an algorithm, via machine learning and using a set of training data, the algorithm ([0054, 0066, 0073 - a portion of the dataset 606 is taken to form a training set 608 in order to identify relevant unstructured and structured variables, and to rank the variables in how predictive they are. This is more easily performed with small datasets as the adaptive learning algorithms can be adjusted quicker, and more easily. At 610, the training is performed where the variables are identified, and a finished training model 612 is outputted with the unstructured and structured variables ranked and ordered in a poly-structured data model. The trained model 612 can then be applied to the larger test set of data 614 during testing) configured to determine user temperament ([0035]; claim 18), the training comprising:
iteratively predicting a ranking of the user temperament, based on the set of training data ([0073] - A portion of the dataset 606 is taken to form a training set 608 in order to identify relevant unstructured and structured variables, and to rank the variables in how predictive they are. This is more easily performed with small datasets as the adaptive learning algorithms can be adjusted quicker, and more easily. At 610, the training is performed where the variables are identified, and a finished training model 612 is outputted with the unstructured and structured variables ranked and ordered in a poly-structured data model), the set of training data comprising volume data, content data (claim 18 and [0032, 0034-0035, 0041] – the dataset based on keywords…emails), inflection data, pitch data, or a combination thereof;
testing and comparing the ranking of the user temperament predicted during each iteration against a target variable ([0073-0074] - a portion of the dataset 606 is taken to form a training set 608 in order to identify relevant unstructured and structured variables, and to rank the variables in how predictive they are. This is more easily performed with small datasets as the adaptive learning algorithms can be adjusted quicker, and more easily. At 610, the training is performed where the variables are identified, and a finished training model 612 is outputted with the unstructured and structured variables ranked and ordered in a poly-structured data model. The trained model 612 can then be applied to the larger test set of data 614 during testing 616; and Structured variables 706, 708, and 710 can include such attributes as application characteristics, fraud scores, previous dispositions, demographic, geographic and survey results. Combinatorial effects of these variables add 18% more lift compared to their respective contributions);
and a feedback loop is shown for the fraud risk management system disclosed herein. Unstructured data 602 and structured data 604 are combined to create a poly-structured dataset 606. A portion of the dataset 606 is taken to form a training set 608 in order to identify relevant unstructured and structured variables, and to rank the variables in how predictive they are ([0073]).
Yeri does not explicitly discuss indicating whether modifications to weights assigned to certain training data are necessary to improve predictability of the ranking of the user temperament.
Saa-Garriga teaches modifications to weights assigned to certain training data ([0086-0087] - the first machine learning algorithm is pre-trained prior to deployment of the system, by training the first machine learning algorithm to generate the different sets of configuration settings defined in step S401. The resulting weights of the first machine teaming are then distributed among users as a ‘global’ model in step S403. Steps S401 to S403 therefore relate to an ‘offline’ training process as described above. In step S404, online training is performed to update the weights in a local copy of the global model, that is, a local model that is stored and trained in a particular configuration setting apparatus and The subset of weights can be selected in step S406 by masking and/or sparsifying the full set of weights of the local model so as to obtain anonymised data that cannot be used to identify a particular user. In this way, a user's privacy can be protected. Each one of the selected users then transmits the subset of weights to the server 130 in step S407, which uses the received weights to update the global model in S408. The updated model can then be pushed to other users' devices in step S403); and receiving data indicative of the user’s actual emotional response to the reproduced content where the second machine learning algorithm is trained to improve an accuracy of future predictions of expected user responses ([0020]).
It would have been obvious before the effective filing date of the claimed invention to indicate whether modifications to weights assigned to certain training data are necessary to improve predictability of the ranking of the user temperament in order to improve an accuracy of future predictions of expected user responses and incorporate the teachings of Saa-Garriga into the teachings of Yeri for the purpose of updating global model based on received modification weights by on device training.
Claim 14 is rejected for the same reasons discussed above with respect to claim 1.
6. Claims 2-4, 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Yeri and Saa-Garriga in view of O’Connor et al. (2015/0358463) and Bohacek et al. (US Patent 6,411,687).
As to claims 2 and 15, Yeri and Saa-Garriga do not explicitly teach the system for determining user temperament according to claims 1 and 7 and the method for determining according to claim 14, further comprising: receiving an incoming call from a user; receive audible language from the user; determine from the audible language of step (b), volume, content, inflection, pitch, or a combination thereof; input the volume, the content, the inflection, the pitch, or combination thereof of step (c) into the algorithm; receive an output, from the algorithm of step (d), wherein the output comprises ranking of the temperament of the user; transfer the incoming call of step (a) to an entity representative, and relay the ranking of the temperament of the user, from step (e) to the entity representative.
O’Connor teaches:
receive an incoming call from a user ([0038] – contacts incoming to the contact center assigned to different queues 208a-n; Fig. 3, step 302 - a communication session with a customer);
receive audible language from the user ([0047] – the server 110 forward a voice contact to an agent; [0055] – detect emotions of the customer from the voice of the customers);
determine, from the audible language of step (b) volume ([0061] – the irate customer shouts in angry manner…), content ([0061]), inflection, pitch, or a combination thereof;
a call from an angry or frustrated customer given a rank 1, happy customers rank 2, and so on ([0055]); and
transfer the incoming call to an entity representative ([0047] – when the server forwards a voice contact to agent the server also forwards customer-related information from the database to the agent’s computer work station for viewing (a pop-up display) to permit the agent to better serve the customer).
O’Connor does not explicitly discuss (d) input the inflection and pitch of step (c) into an algorithm configured to rank a temperament of the user and (e) receive an output from the algorithm of step (d), (g) relay the ranking of the temperament of the user to the entity representative.
Bohacek teaches the mood detector monitors the caller’s voice and analyzing the phonemes or other voice characteristics such as rapidity of speech, loudness or quickness of response to determine the degree of the caller’s annoyance or impatience and these parameters sent to the mood logic; the pitch extracted with a pitch detector and combining all information to determine the potential degree of the caller’s annoyance, the degree of impatience and also sent these parameters to the mood logic (col. 2, line 47 through col. 3, line 16); when the annoyance level exceeds a preset threshold determined by the mood logic the call routes to a set of agents that are good at dealing with annoyed customers (col. 3, lines 17-21) and the degree of potential annoyance of impatience determined by the mood logic displayed on the screen of the agent’s PC to indicate the potential degree of the caller’s mood (col. 3, lines 23-26). It is well-known to determine emotion based on pitch and inflection.
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of O’Connor and Bohacek into the teachings of Yeri and Saa-Garriga for the purpose of ranking temperament or sentiments of the users based on words detected in the audible language with users or customers and better serve the customer.
As to claims 3 and 16, Bohacek teaches the system for determining user temperament according to claim 2 and the method for determining and processing user temperament according to claim 15 wherein the entity representative is selected based on the ranking of the temperament of the user (col. 3, line 65 through col. 4, line 2 – the IVR asks the caller for information, the same speech samples and touch tone inputs are analyzed for the mood of the caller and the call is routed to the next available agent that is good at handling this kind of call); and the mood logic unit such as a neural net device that combines all the available information to determine the potential degree of the caller’s annoyance, the degree of impatience and this information is sent to the ACD before the call is switched to an agent (col. 3, lines 10-16).
As to claims 4 and 18, O’Connor teaches the system for determining user temperament according to claim 1 and the method for determining and processing user temperament according to claim 14 wherein the executable code further causes the processor to monitor the transferred incoming call ([0047] – when the server forwards a voice contact to agent; and [0070, 0077] – continuing to monitor interactions between the customer and the allocated resources to determine whether any progress is being achieved corresponding to the problems identified by the customer by monitoring if the anger of the customer is improving or further degrading; [0084] – monitor the interactions between the customer and the allocated resources to determine whether or not the customer is being satisfied with the communication).
As to claim 17, Bohacek teaches the method for determining and processing user temperament according to claim 15 wherein the method further comprises providing the entity representative with a script corresponding to the ranking of the temperament of the user (col. 3, lines 10-16 - the mood logic unit such as a neural net device that combines all the available information to determine the potential degree of the caller’s annoyance, the degree of impatience and this information is sent to the ACD before the call is switched to an agent; col. 3, line 60 through col. 4, line 7 - the IVR asks the caller for information, the same speech samples and touch tone inputs are analyzed for the mood of the caller and the call is routed to the next available agent that is good at handling this kind of call and a caller who is in a hurry will be routed to a trained agent and a message indicating this is flashed on the agent’s screen).
7. Claims 5-6, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yeri, Saa-Garriga, O’Connor and Bohacek in view of Palandurkar et al. (2021/0273980).
As to claims 5 and 19, Yeri, Saa-Garriga, O’Connor and Bohacek do not explicitly discuss the system for determining user temperament according to claim 2, the system for processing user temperament according claim 9, and the method of claim 15 wherein the executable code further causes the processor to input call history of the user into the algorithm.
Palandurkar teaches input call history of the user into the algorithm ([0124] - server system 210 includes a sentiment parameter index analyzer to analyze sentiment and urgency of the user based on the set of interactions; the sentiment analysis provided based on keywords detected in the call and the positive or negative ranks provided to the corresponding keywords and based on the ranking sentiments of the users determined); and providing recommendations to the users based on their conversation history ([0038]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Palandurkar into the teachings of Yeri, Saa-Garriga, O’Connor and Bohacek for the purpose of analyzing the sentiment and urgency of the user’s query from call history based on the set of interactions.
As to claims 6 and 20, Palandurkar teaches the system for determining user temperament according to claim 2, the system for processing user temperament according claim 9, and the method of claim 15 wherein the ranking of the temperament of the user is determined using a number scale ([0124] and table I); and O’Connor teaches a call from an angry or frustrated customer given a rank 1, happy customers rank 2, and so on ([0055]).
8. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Yeri et al. (2021/0097605) in view of Saa-Garriga et al. (2021/0065649) and Erbey et al. (2012/0317038).
Claim 7 is rejected for the same reasons discuss above with respect to claim 1. Yeri and Saa-Garriga do not teach data comprising volume data, inflection data, and pitch data.
Erbey teaches emotion detection system monitors one or more of the customer’s pitch, tone, tempo and inflection to detect the state of the customer’s emotion ([0205]).
It would have been obvious before the effective filing date of the claimed invention to incorporate the teachings of Erbey into the teachings of Yeri and Saa-Garriga for the purpose of detecting and ranking user’s emotion or temperament based on inflection, pitch, and tone of the user.
9. Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yeri, Saa-Garriga, and Erbey in view of O’Connor et al. (2015/0358463) and Bohacek et al. (US Patent 6,411,687).
Claim 8 rejected for the same reasons discussed above with respect to claim 2. Furthermore, Erbey teaches emotion detection system monitors one or more of the customer’s pitch, tone, tempo and inflection to detect the state of the customer’s emotion ([0205]).
As to claim 9, Bohacek teaches the system for determining user temperament according to claim 8 wherein providing the entity representative with a script corresponding to the ranking of the temperament of the user (col. 3, lines 10-16 - the mood logic unit such as a neural net device that combines all the available information to determine the potential degree of the caller’s annoyance, the degree of impatience and this information is sent to the ACD before the call is switched to an agent; col. 3, line 60 through col. 4, line 7 - the IVR asks the caller for information, the same speech samples and touch tone inputs are analyzed for the mood of the caller and the call is routed to the next available agent that is good at handling this kind of call and a caller who is in a hurry will be routed to a trained agent and a message indicating this is flashed on the agent’s screen).
As to claim 10, Bohacek teaches the system for determining user temperament according to claim 9 wherein the entity representative is selected based on the ranking of the temperament of the user (col. 3, line 65 through col. 4, line 2 – the IVR asks the caller for information, the same speech samples and touch tone inputs are analyzed for the mood of the caller and the call is routed to the next available agent that is good at handling this kind of call); and the mood logic unit such as a neural net device that combines all the available information to determine the potential degree of the caller’s annoyance, the degree of impatience and this information is sent to the ACD before the call is switched to an agent (col. 3, lines 10-16).
As to claim 11, O’Connor teaches the system for determining user temperament according to claim 9 wherein the executable code further causes the processor to monitor the transferred incoming call ([0047] – when the server forwards a voice contact to agent; and [0070, 0077] – continuing to monitor interactions between the customer and the allocated resources to determine whether any progress is being achieved corresponding to the problems identified by the customer by monitoring if the anger of the customer is improving or further degrading; [0084] – monitor the interactions between the customer and the allocated resources to determine whether or not the customer is being satisfied with the communication).
10. Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Yeri, Saa-Garriga, Erbey, O’Connor and Bohacek in view of Palandurkar et al. (2021/0273980).
Claims 12 and 13 are rejected for the same reasons discussed above with respect to claims 5 and 6.
Double Patenting
11. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
12. Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12,243,555. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in the present application are broader and transparently found in the U.S. Patent 12,243,555 with obvious wording variations. When claims in the pending application are broader than the ones in the patent, the broad claims in the pending application are rejected under obviousness type double patenting over previously patented narrow claims, In re Van Ornum and Stang, 214 USPQ 761. Also, omission of an element and its function in a combination is an obvious expedient if the remaining elements perform the same functions as before. In re KARLSON (CCPA) 136 USPA 184 (1963).
U.S. Patent Application 19/023,590
US Patent 12,243,555
A system for determining user temperament comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to:
A system for determining user temperament comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and a memory device storing executable code that, when executed, causes the at least one processor to:
train an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user temperament, the training comprising:
(a’) train an algorithm, via machine learning and using a set of training data, the algorithm configured to determine user temperament, the training comprising:
iteratively predicting a ranking of the user temperament, based on the set of training data, the set of training data comprising volume data, content data, inflection data, pitch data, or a combination thereof;
iteratively predicting a ranking of the user temperament, based on the set of training data, the set of training data comprising volume data, content data, inflection data, pitch data;
(ii) testing and comparing the ranking of the user temperament predicted during each iteration against a target variable; and
(ii) testing and comparing the ranking of the user temperament predicted during each iteration against a target variable; and
(iii) indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain training data are necessary to improve predictability of the ranking of the user temperament.
(iii) indicating, via a feedback loop, for each iteration whether modifications to weights assigned to certain training data are necessary to improve predictability of the ranking of the user temperament.
receive and incoming call from a user;
receive audible language from the user;
determine, from the audible language of step (b), volume, content, inflection, and pitch;
input the volume, the content, the inflection, and the pitch of step (c),into the algorithm;
receive an output, from the algorithm of step (d), wherein the output comprises a ranking of temperament of the user;
transfer the incoming call of step (a) to an entity representative, and
relay the ranking of the temperament of the user, from step (e), to the entity representative.
The examiner also notes that claims 3, 4, 5, 6, 7, 10, 11, 12, 13, 14,16, 17, 18, 19, and 20 of the ‘590 Application corresponds to Claims 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, and 16 of the ‘555 patent.
Conclusion
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693
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