DETAILED ACTION
Claims 1, 3 – 8, 10 – 15, 17 – 22, and 24 – 27 are pending.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 2026 has been entered.
Response to Amendment
With regard to the Non-Final Office Action from 12 June 2025, the Applicant has filed a response on 22 September 2025.
The Examiner maintains the double patenting rejection despite the amendment to the independent claims. The Applicant indicated (Remarks: page 7 par 2) that the double patenting rejection should be held in abeyance.
Response to Arguments
With regard to the 35 U.S.C. 101 rejection of the claims being dependent upon a judicial exception without significantly more, the Applicant indicates (Remarks: page 7 par 5) that the presented analysis is flawed, its rejection being traversed. It is indicated (Remarks: page 8 par 1) that the claims do not fall within any of the three main categories, in the situation of the most-recent Office Action — a mental process. It is stated here (Remarks: page 8 par 2) that the claims provide for an improvement in the functioning of computers or other technology, that the end result is not a mere calculation/determination, that the individual claim elements integrated into a practical application, all these being an improvement of the overall operation of the computer system. This appears to be further seen by the Applicant’s statement that ‘the electronic social agent is made into a better electronic social agent, i.e., a better machine’ (remarks: page 9 par 2). The Applicant then indicates (Remarks: page 9 par 3) that a computer is improved by having better software run on it, in this case, a better decision making model. It is indicated that the claims need not state or specify the improvement (Remarks: page 10 par 1–3), and the Examiner is inclined to agree with the Applicant in this situation about the claims not having to particularly state the improvement. To this, the Examiner states that, despite the indicated improvement the invention provides to a computer, the claims as currently (and as previously) presented, are still very much directed to a judicial exception.
Considering claim 1, this claim is directed to processing a first user input (a human can read or listen to a first input to try to understand it), determine that a second user input is required by processing the first input (the human determines that the first user input is not enough and further information would be required), collecting the second user input after it has been determined that the second user input is required (the human receives the second user input as the further required information, this could be received visually or auditorily), updates a decision-making model based on the second user input (the human may write down more information regarding the type of decisions to be made based on the received second user input, so that such information may be applied to other future scenarios), and in a situation where the second user input is not required, also updating the decision-making process (whereby in the absence of requiring the second user input, the human may write down more information regarding the type of decisions to be made based on just the first user input). This here is the main process being performed by the claim. The electronic social agent appears to be a platform that this technique is performed in. The machine learning algorithm performs the task of decision-making, which, as explained, is what a human would use the human-mind for, leading to the classification of the claim as a mental process, with each limitation here being one that can be mentally performed by a human.
The claims explicitly require the use of a machine learning algorithm, and a machine learning decision-making model for the updating now (Remarks: page 11 par 1), with the indication that humans do not maintain nor update such models. The Examiner indicates first that the machine learning models are still simply tools used for performing the mental process. A human can write down a model as a technique comprising a series of steps to be taken to achieve a goal or perform an action. Indicating a machine learning model simply indicates the presence of a particular model/technique that can be mentally performed, the machine learning model being a took for performing the technique. Adding notes to the written-down steps is a way of updating the technique, which is akin to the updating of the machine learning model. The Applicant further attempts to differentiate the updating of the machine learning model from what a human would do (Remarks: page 11 par 3), presenting the two different situations and stating that a human wouldn’t update by both paths, especially when data is absent. But, in the situation that data is absent, the absent data being the second user input, a human may still update a model based on the information obtained from the first user input, as is clearly provided by the final limitation of claim 1, or based on the collected second user input, as is also indicated in claim 1. Updating a model based on the available information is routine in the art (Remarks: page 11 par 4). A human may therefore update a model as a series of steps for a technique based on the information obtained from either the second user input, or the first user input, both of these being new data, and not particularly different from what people would normally do (Remarks: page 12 par 3).
The Applicant indicates that (Remarks: page 12 par 5) this technique is akin to self-programming, integrating it into a practical application, thereby directly affecting the operation of the system. The Examiner maintains again that, each of the limitations provided here are by themselves, mental processes, and do not contain further limitations or additional elements that integrate the abstract idea into a practical application.
The Examiner agrees that a person is not an electronic social agent (Remarks: page 12 par 6) but a person does engage in decision-making techniques, which, as explained above regarding this claim, a human is able to perform. The machine learning model is simply a tool being applied here to perform the task on a computer.
The Applicant refers to the Enfish decision (Remarks: page 12 par 1 – page 13 par 3) to indicate that a human’s ability to perform the steps using a pencil and paper, but it being claimed to be performed in software can still be found eligible. The Examiner explains here that for the decision of the instant claims currently being considered, the claims are directed to a mental process without significantly more.
The Applicant argues against the Examiner’s 35 U.S.C. 103 rejection, focusing first on indicating that the Examiner’s references do not teach about updating the decision-making model when it is determined that additional input is not to be collected (Remarks: page 15 par 2). The Examiner here addresses this slightly as, when the first user input is collected, updating the model can make use of just the information obtained from the first user input. When a second user input is also collected, updating the model can make use of the information collected from the second user input. The claims teach of updating a model in the presence of new information, which is what is required for an update to take place. The Examiner will address the specificities in the appropriate rejection section.
The Applicant argues further that the machine-learning model of the Wangikar reference differs from that of the claimed invention (Remarks: page 16 par 4) based on them being from different domains, especially with Wangikar not being directed to an electronic social agent. The Examiner maintains for the purpose of this argument here that the Dahan et al. reference is applied to the teaching of a chatbot, which qualifies as an electronic social agent, and this is being used in combination with the Wangikar reference to teach the claimed invention. Having Wangikar teach a decision-making ML model different from that of claimed invention does not disqualify it from being useful as being obvious, in combination with another reference, to teach the claimed invention.
The Applicant indicates that the model-updating of the Wangikar reference is ‘always based on new information’ (Remarks: page 17 par 1) different from that of the claimed invention, and that ‘there is no teaching or suggestion by Wangikar of updating a model based on a determination that additional input will not be collected as is called for in the claims.’ The Examiner however states here that the independent claims themselves do provide the updating of the decision-making model based on the collection of new information, one being the second user input while the other being the first user input. The Applicant’s difference here seems to be that the first updating in the performed in the absence of a second user input, but does not actually indicate that no new information is obtained as the claims indicate that first user input is collected. The Examiner will address this as well in the further rejection section.
The Applicant argues against the Dechu et al. reference (Remarks: page 17 par 2 – 3) that this reference is unrelated to the other references, this one being directed to updating a conversational model, not a decision-making model. To this, the Examiner indicates that it has been held that a prior art reference must either be in the field of the inventor’s endeavour or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, the Dechu et al. reference addresses the model-updating, a situation provided by the claims themselves, and particularly addressed by FIG. 1 of the Dechu et al. reference. The Applicant states that this reference teaches an iterative process, but does not teach ‘determining that generation of the at least one second user input is not required …’ The Examiner disagrees with this notion by the Applicant. The Examiner refers to this Dechu et al. in Figure 1 and [0019], agreeing that it does provide an iterative step, but further indicating that, in a situation where a disambiguation has to be made, the system may provide a question to the user, so as to collect a ‘second user input’ and after the final node has been reached at Step 106, then system updates the state disambiguation model at Step 108. Consider the same Figure 1, in a situation whereby a disambiguation is not required, the state disambiguation model is still updated based on the available information. The iterative nature of this reference does not remove from teaching the claimed invention, as this reference provides updating the model in the situation where a disambiguation to obtain a second user input is not required, and in a situation where a disambiguation to obtain a second user input is required. The reaching of the final node in this situation is an indication that a disambiguation would no longer be required, further indicating that the system would not require a ‘second user input.’ The Applicant states (Remarks: page 18 par 1 – 3) that this reference refers to a ‘teaching to update after the interaction is complete and a final node is reached’ but the Examiner indicates here also that, reaching a final node would be akin to needing no further disambiguation and thereby not needing to request any more ‘second user input’ from the user.
For the sake of moving the prosecution forward though, the Examiner acquiesces and indicates here that the independent claims will be addressed by their current presentation.
Double Patenting
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.
Instant claim 1 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because they are both directed to receiving a first user input and applying a machine learning algorithm to determine that it is required to collect a second user input, collecting the second user input through at least one sensor, and then updating the decision-making model of the electronic social agent based on the collected second user input. The instant claim fails to teach the limitation of ‘updating the decision-making model of the electronic social agent based on the first user input, upon determination that generation of the at least one second user input is not required.’ This is however taught by the reference of Kim et al. (US 2019/0355351 A1) as provided by [0034] and [0050], teaching a determination not to request feedback from a user, and the updating of a mapping unit. It would have been obvious to one of ordinary skill in the art to incorporate this reference based on the predictable result of keeping each state of the conversation between the user and the social agent up-to--date so that the system knows what type of response to provide at any point in the conversation, as well as improving the decision-making for better accuracy .
Instant claim 3 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 4 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 2 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 5 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 6 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 7 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 8 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 1 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 10 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 7 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 11 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 8 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 12 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 9 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 14 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 10 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 15 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 17 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 18 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 12 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 19 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 21 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 22 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 11 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 24 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 17 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 25 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 18 of U.S. Patent No. 11,907,298 B2 in view of Kim et al. (US 2019/0355351 A1).
Instant claim 27 is rejected on the ground of nonstatutory double patenting as being obviously unpatentable over claim 9 of U.S. Patent No. 11,907,298 B2 (claim 9 here being directed to a method instead of the system of instant claim 27) in view of Kim et al. (US 2019/0355351 A1). One of ordinary skill in the art would have found it obvious to modify steps provided by the method of claim 9 to arrive at the system of claim 27, given the predictable result of performing the technique on a device to sort out possible software bugs and glitches, while making the product available to people on devices accessible to them.
Instant Application
U.S. 11,907,298 B2
Claim 1
Claim 1
A method for managing an electronic social agent, comprising:
applying at least one process to a first user input, wherein the first user input is collected by the electronic social agent;
A method for updating a decision-making model of an electronic social agent by actively collecting at least a user response, comprising:
collecting in near real-time at least a portion of a first dataset and at least a first user response with respect to the performed action, wherein the first dataset indicates at least a current state;
determining based on the first user input whether collection of at least one second user input is required, wherein at least one process is used in determining whether collection of the at least one second user input is required, the at least one process employing at least one machine learning (ML) algorithm;
applying at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
collecting, using one or more sensors, the at least one second user input upon determination that the collection of at least one second user input is required
collecting, using at least one sensor the at least a second user response
updating a machine learning decision-making model of the least one ML algorithm of the electronic social agent based on the at least one collected second user input
updating the decision-making model of the electronic social agent based on the collected at least a second user response.
updating the decision-making model of the electronic social agent based on the first user input, upon determination that generation of the at least one second user input is not required.
As provided by the reference of Kim et al.
Claim 3
Claim 1
The method of claim 1, wherein the at least one process is applied to at least a portion of a first dataset collected by the electronic social agent, wherein the first dataset indicates at least a current state.
collecting in near real-time at least a portion of a first dataset and at least a first user response with respect to the performed action, wherein the first dataset indicates at least a current state
Claim 4
Claim 2
The method of claim 3, wherein the current state is associated with a user and an environment of the user, wherein the at least portion of the first dataset is collected using the one or more sensors that are connected to the electronic social agent.
The method of claim 1, wherein the current state is associated with a user and an environment of the user, wherein the at least a portion of the first dataset is collected using one or more sensors that are communicatively connected to the electronic social agent.
Claim 5
Claim 1
The method of claim 3, further comprising: determining based on the first dataset and the first user input, whether the collection of at least one second user input is required.
applying at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
Claim 6
Claim 1
The method of claim 1, further comprising: generating at least one question for collecting the at least one second user input.
generating a question for collecting the at least a second user response;
Claim 7
Claim 1
The method of claim 6, further comprising: presenting the at least one question to the user.
presenting, by the electronic social agent, the at least a question to the user at the determined optimal time;
Claim 8
Claim 1
The method of claim 1, wherein the at least one process is a machine learning (ML) algorithm.
applying at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
Claim 10
Claim 7
The method of claim 1, wherein the one or more sensors are virtual sensors which receive inputs from online sources.
The method of claim 2, wherein the one or more sensors are virtual sensors which receive inputs from online sources.
Claim 11
Claim 8
The method of claim 1, further comprising: applying a pre-determined threshold to determine whether it is required to collect the at least one second user input.
The method of claim 1, wherein determining whether the collection of at least a second user response is desirable further comprises:
applying a pre-determined threshold to determine whether it is desirable to collect a second user response.
Claim 12
Claim 9
The method of claim 1, wherein the at least one second user input is at least one of: a verbal input, and a non-verbal input.
The method of claim 1, wherein the at least a second user response is at least one of: a verbal response, and a non-verbal response.
Claim 14
Claim 10
A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry for managing an electronic social agent, the process comprising:
applying at least one process to a first user input, wherein the first user input is collected by the electronic social agent;
A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry for updating a decision-making model of an electronic social agent by actively collecting at least a user response, the process comprising:
determining based on the first user input whether collection of at least one second user input is required, wherein at least one process is used in determining whether collection of the at least one second user input is required, the at least one process employing at least one machine learning (ML) algorithm;
applying at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
collecting, using one or more sensors, the at least one second user input upon determination that the collection of at least one second user input is required;
collecting, using at least one sensor the at least a second user response;
updating a machine learning decision-making model of the least one ML algorithm of the electronic social agent based on the at least one collected second user input; and
updating the decision-making model of the electronic social agent based on the collected at least a second user response.
updating the decision-making model based on the first user input, upon determination that generation of the at least one second user input is not required.
As provided by the reference of Kim et al.
Claim 15
Claim 11
A system for managing an electronic social agent, comprising:
a processing circuitry; and
A system for updating a decision-making model of an electronic social agent by actively collecting at least a user response, comprising:
a processing circuitry; and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
apply at least one process to a first user input, wherein the first user input is collected by the electronic social agent;
collect in near real-time at least a portion of a first dataset and at least a first user response with respect to the performed action, wherein the first dataset indicates at least a current state;
determine based on the first user input whether collection of at least one second user input is required, wherein at least one process is used in determining whether collection of the at least one second user input is required, the at least one process employing at least one machine learning (ML) algorithm;
apply at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
collect, using one or more sensors, the at least one second user input upon determination that the collection of at least one second user input is required;
collect, using at least one sensor the at least a second user response;
update a machine learning decision-making model of the least one ML algorithm of the electronic social agent based on the at least one collected second user input; and
update the decision-making model of the electronic social agent based on the collected at least a second user response.
update the decision-making model based on the first user input, upon determination that generation of the at least one second user input is not required.
As provided by the reference of Kim et al.
Claim 17
Claim 11
The system of claim 15, wherein the at least one process is applied to at least a portion of a first dataset collected by the electronic social agent, wherein the first dataset indicates at least a current state.
collect in near real-time at least a portion of a first dataset and at least a first user response with respect to the performed action, wherein the first dataset indicates at least a current state;
Claim 18
Claim 12
The system of claim 17, wherein the current state is associated with a user and an environment of the user, wherein the at least portion of the first dataset is collected using the one or more sensors that are connected to the electronic social agent.
The system of claim 11, wherein the current state is associated with a user and an environment of the user, wherein the at least a portion of the first dataset is collected using one or more sensors that are communicatively connected to the electronic social agent.
Claim 19
Claim 11
The system of claim 17, wherein the system is further configured to: determine based on the first dataset and the first user input, whether the collection of at least one second user input is required.
apply at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
Claim 20
Claim 11
The system of claim 15, wherein the system is further configured to: generate at least one question for collecting the at least one second user input.
generate a question for collecting the at least a second user response;
Claim 21
Claim 11
The system of claim 20, wherein the system is further configured to: present the at least one question to the user.
present, by the electronic social agent, the at least a question to the user at the determined optimal time;
Claim 22
Claim 11
The system of claim 15, wherein the at least one process is a machine learning (ML) algorithm.
apply at least one algorithm, wherein the at least one algorithm is a machine learning (ML) algorithm, to determine, based on the first dataset and the first user response, whether collection of at least a second user response is desirable;
Claim 24
Claim 17
The system of claim 15, wherein the one or more sensors are virtual sensors which receive inputs from online sources.
The system of claim 12, wherein the one or more sensors are virtual sensors which receive inputs from online sources.
Claim 25
Claim 18
The system of claim 15, wherein the system is further configured to: apply a pre-determined threshold to determine whether it is required to collect the at least one second user input.
The system of claim 11, wherein the system is further configured to:
apply a pre-determined threshold to determine whether it is desirable to collect a second user response.
Claim 27
Claim 9 (dependent on claim 1)
The system of claim 15, wherein the at least one second user input is at least one of: a verbal input, and a non-verbal input.
The method of claim 1, wherein the at least a second user response is at least one of: a verbal response, and a non-verbal response.
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 – 8, 10 – 15, 17 – 22, and 24 – 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Independent claims 1, 14 and 15 recite the limitations of managing an electronic social agent by processing a first user input, determining based on the first user input if it is required to collect a second user input, this determination being performed by at least one process that employs an ML algorithm, when it is determined that it is required to collect the second user input, collecting the second user input, updating an ML decision-making model based on the at least one collected second user input, and based on the determination that a second user input is not required, updating the decision-making model based on the first user input.
Nothing in the claims precludes the claims from being performed in the human mind. The entire process involves data collection and data analysis. A human, acting as the electronic social agent (which typically would require maintaining a conversation or performing an action based on an input) may receive a first user input, analyse that first user input to determine if the collection of a further input would be required, and if required, the human would collect a second user input, the human then writes, on a note that contains information regarding the decision-making process, further information received from the second user input, so that information on the second user input can be applied to the overall decision-making technique, and, in the situation where a second user input is not required, the human writes, on the note that contains information regarding the decision-making process, further information received from the first user input, so that information on the first user input can be applied to the overall decision-making technique. The claims hereby recite a mental process.
This judicial exception is not integrated into a practical application as the claims simply teach of collecting data in the form of collecting the first and second user inputs, and through updating the decision-making model under both situations; and analysing data in the form of determining if collecting the second user input would be required. The mentioned machine learning algorithm, processing circuitry, and memory are also recited in generic terms.
The invention is not tied to any particular defining structure and simply provides instructions to apply the judicial exception. The technique can be performed by a generic computer which would be presented as a tool to implement the abstract idea (classifiable as automation of the mental process steps). The Specification in [18] shows the implementation of the electronic social agent on a computer, which can be a generic computer. The claim also refers to an electronic social agent which, in [3], is provided as software systems that provide simplification of the user experience. By its current presentation, it appears to be an application on a computer that is able to perform simplify user experience, a function that is routine and well-known in the art, and can also be performed by a human. The one or more sensor used for collecting at least one second user input serve as general-purpose sensors utilised for collecting the necessary data (data gathering) and serve as additional elements but are not yet sufficient to amount to significantly more than the mentioned judicial exception. The Specification makes mention of a machine learning algorithm that is based on a machine learning model that can determine and generate a question for collecting the second user response [33]. Neither the claims nor the Specification provide specifics on which machine learning model is being used, or the way it is being applied to perform the mentioned tasks in the claims. Mentioning a machine learning model/algorithm can simply refer to the application of a general-purpose computer to perform a mental process, as used here, of determining if the collection of a second user input would be required, which is a mental process. A human may perform the function of the machine learning model by applying the human mind to reason out if a second user input would be required for the task that was intended by the first user input. These electronic social agent, processing circuitry, memory, one or more sensors, and machine learning model, are recited at a high level of generality that it amounts to no more than mere instructions to apply the exception using a generic computer. The claims do not include any additional element that would that would be sufficient to amount to significantly more than the judicial exception because the invention is not tied to a practical application.
The claims provide techniques that amount to no more than mere instructions that apply the judicial exception which can be performed by a generic device. Merely mentioning the processing circuitry and memory amounts to no more than general-purpose hardware used as tools to implement the abstract idea and does not provide any particular application other than applying it for the purpose of implementing a judicial exception. Mere instructions to apply an exception using a generic device cannot provide an inventive concept. Claims 1, 14 and 15 are not eligible.
Claims 3 and 17 the application of a process to at least a portion of a first dataset collected by the electronic social agent with the first dataset indicating at least a current state. A human may simply collect information about a state of a conversation.
Claims 4 and 18 provide that the current state is associated with a user and the user’s environment, the first dataset being collected using sensors connected to the electronic social agent. The one or more sensors simply serve as additional tools for data gathering and a user through observation may collect state information about the user and the user’s environment. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 5 and 19 provide determining if it is required to collect at least one second user input based on the first dataset and the first user input. A human may analyse the available user information to determine if it would be required to obtain further information from the user. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 6 and 20 provide generating at least one question for collecting at least one second user input. A human may generate a question to collect further user input. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 7 and 21 provide presenting the at least one question to the user. A human may present a generated question to the user by pen and paper. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 8 and 22 provide that the at least one process is a machine learning algorithm. The Specification in [33] simply provides a generic machine learning algorithm without any specifics on how it is implemented. Having a machine learning algorithm serves as mathematical tool for implementing the abstract idea. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 10 and 24 provide that the sensors are virtual receiving inputs form online sources. A human may receive further information about a situation from another human at a different location. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 11 and 25 provide applying a pre-determined threshold to determine whether to collect the at least one second user input. A human may apply a mental threshold to a first user input to determine if to request a second user input. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 12 and 27 provide that the at least one second user input is at least one of verbal or non-verbal input. The user being a human can provide either verbal or non-verbal input, which would be understood by a second human. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claims 13 and 26 provide execution of one or more action based on at least the second user input. A human may perform an action that clarified by a second user input. This does not integrate any practical application nor does it provide any additional element sufficient to amount to more than the mentioned judicial exception.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3, 6, 7, 8, 12, 13, 14, 15, 17, 20, 21, 22, 26 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Dahan et al. (US 2018/0054524 A1: hereafter — Dahan) in view of Kim et al. (US 2019/0355351 A1: hereafter — Kim).
For claim 1, Dahan discloses a method for managing an electronic social agent (Dahan: [0016] — engaging in a conversation with chatbot (the chatbot here is taken as the claimed electronic social agent; consider also [0016] which provides that a decision engine able to perform one or more actions based on a conversation with a user, to provide teaching of an electronic social agent that is able to perform an )), comprising:
applying at least one process to a first user input, wherein the first user input is collected by the electronic social agent (Dahan: [0084] — applying a natural language processing to a first user input (the voice command here can be taken as the first user input); [0018] — a chatbot which engages in a conversation with the user (indicating that the chatbot (electronic social agent) receives the first user input));
determining based on the first user input whether collection of at least one second user input is required, wherein at least one process is used in determining whether collection of the at least one second user input is required, the at least one process employing at least one machine learning (ML) algorithm (Dahan: [0084] — applying a natural language processing to the user’s input and an AI unit handling the user’s request; [0085] — an AI unit that is able to determine that a request for clarification is required and creates the request for clarification to be present to the user (indicating the use of the NLP and an AI unit as a machine learning algorithm used to determine that at least a second user input is required); [0080] — prompting the user to enter or utter a request (indicating the presence of a sensor to receive the user’s input));
collecting, using one or more sensors, the at least one second user input upon determination that the collection of at least one second user input is required (Dahan: [0080] — prompting the user to enter or utter a request (indicating the presence of a sensor to receive the user’s input); [0053] — the system comprises an interface with a chat channel, voice channel and telephone module, these being used to engage a user with a request and that a user can connect using a telephone module (all these indicate the presence of a sensor for receiving the user’s input); [0083] — a local interface that can receive a voice command (indicating the presence of a sensor for receiving the user’s input); [0113] — receiving feedback from a user).
The reference of Dahan provides teaching for a decision-making model that receives a user input and applies a machine learning algorithm to determine that a further user input would be required of the user, leading to then collecting the second user input. This reference however fails to teach of updating a machine learning decision-making model after having processed the user inputs.
This however isn’t new to the art as the reference of Yu is now introduced to teach this as
updating a machine learning decision-making model of the least one ML algorithm of the electronic social agent based on the at least one collected second user input (Kim: [0018] — initiating the performance of a task based on a received first user input; [0033] — prompting a user for feedback (a determination that a second user input is required) in a situation where a skill was not properly identified; [0020], [0035] — updating a mapping unit based on user feedback (based on the second user input); [0040] — the mapping unit may include a convolutional neural network (teaching a machine learning model));
updating the decision-making model of the electronic social agent based on the first user input, upon determination that generation of the at least one second user input is not required (Kim: [0018] — initiating the performance of a task based on a received first user input; [0034] — determining not to request feedback from a user when it is determined that that the user is having a non-negative experience (not seeking a second user input); [0050] — updating a mapping unit based on a received user’s first input command; [0040] — the mapping unit may include a convolutional neural network (teaching a machine learning model)).
The reference of Dahan provides teaching for a decision-making model that receives a user input and applies a machine learning algorithm to determine that a further user input would be required of the user, leading to then collecting the second user input, but differs from the claimed invention in that the claimed invention further provides teaching for updating a decision-making model of the ML algorithm based on either the collected second user input, or in the absence of requiring a second user input, updating based on the first user input. This isn’t new to the art as the reference of Kim is introduced to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to improve upon the technique provided by Dahan which provides a decision making model based on user input, by applying the known teaching of Kim which further updates the decision-making model based on either collected user feedback, or in the absence of requiring a second user input, updating based on the first user input, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of improving and controlling the model’s decision-making process, making it more accurate and more attuned to the user’s preferences. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
For claim 3, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, wherein the at least one process is applied to at least a portion of a first dataset collected by the electronic social agent (Dahan: [0016] — extracting attached data from the chat conversation; [0127] — collecting session data (as another portion of a first dataset collected by the electronic social agent)), wherein the first dataset indicates at least a current state (Dahan: [0029] — carrying over session information (the session information here is indicative of the current state)).
For claim 6, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, further comprising:
generating at least one question for collecting the at least one second user input (Dahan: [0080], [0085] — creating and prompting the user to enter or utter a request).
For claim 7, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, further comprising:
presenting the at least one question to the user (Dahan: [0080], [0085] — creating and prompting the user to enter or utter a request).
For claim 8, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, wherein the at least one process is a machine learning (ML) algorithm (Dahan: [0089] — applying deep learning (which is a machine learning model)).
For claim 12, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, wherein the at least one second user input is at least one of: a verbal input, and a non-verbal input (Dahan: [0014] — chatbot that receives user input; [0085] — the AI unit that creates a request for clarification from the user; [0080] — receiving a speech request (indicating a verbal input)).
For claim 13, claim 1 is incorporated and the combination of Dahan in view of Kim discloses the method, further comprising:
executing one or more actions by the electronic social agent based on the at least one second user input (Dahan: [0116] — after feedback (the second user input), it can show that a course of action turns out to be satisfactory (indicating the execution of an action)).
As for claim 14, computer program product claim 14 and method claim 1 are related as a computer program product storing executable instructions required for performing the claimed method steps on a computer. Dahan in [0079] provides a mobile device like a laptop/computer or tablet, and in [0021] provides a computerised method for implementing a chatbot conversation, inherently showing the storage of the system instructions, and these are able to read upon the limitations of this claim. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to method claim 1.
As for claim 15, system claim 15 and method claim 1 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Dahan in [0079] provides contain a processor, and in [0146] provides data storage, these being suitable to read upon the limitations of this claim. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to method claim 1.
As for claim 17, system claim 17 and method claim 3 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to method claim 3.
As for claim 20, system claim 20 and method claim 6 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to method claim 6.
As for claim 21, system claim 21 and method claim 7 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 21 is similarly rejected under the same rationale as applied above with respect to method claim 7.
As for claim 22, system claim 22 and method claim 8 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 22 is similarly rejected under the same rationale as applied above with respect to method claim 8.
As for claim 26, system claim 26 and method claim 13 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 26 is similarly rejected under the same rationale as applied above with respect to method claim 13.
As for claim 27, system claim 27 and method claim 12 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 27 is similarly rejected under the same rationale as applied above with respect to method claim 12.
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dahan (US 2018/0054524 A1) in view of Kim (US 2019/0355351 A1) as applied to claims 3 and 17, and further in view of Lecaros Easton et al. (US 2021/0026896 A1: hereafter — Lecaros Easton).
For claim 4, claim 3 is incorporated but the combination of Dahan in view of Kim fails to disclose the limitation of this claim, for which Lecaros Easton is now introduced to teach as
the method, wherein the current state is associated with a user and an environment of the user, wherein the at least portion of the first dataset is collected using the one or more sensors that are connected to the electronic social agent (Lecaros Easton: [0045] — sensor data received from the hardware sensor on a bot; [0067] — receiving at the bot, contextual data as well as speech data; [0064]–[0065] — contextual data from sensors that are in the environment).
The combination of Dahan in view of Kim provides teaching for obtaining a portion of a first dataset as current state information, but differs from the claimed invention in that the claimed invention further provides teaching for obtaining current state information, but differs from the claimed invention in that the claimed invention further provides teaching for obtaining current state information as that associated with the user and an environment of the user. This isn’t new to the art as the reference of Lecaros Easton is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the known teaching of Lecaros Easton which obtains information associated with the user and the user’s environment through sensors, with the teaching of the combination of Dahan in view of Kim which just teaches of obtaining current state information, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of delivering responses to a user in a manner that considers the environmental situation of the user. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 18, system claim 18 and method claim 4 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to method claim 4.
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dahan (US 2018/0054524 A1) in view of Kim (US 2019/0355351 A1) as applied to claims 3 and 17, and further in view of PETILL et al. (US 2021/0012113 A1: hereafter — Petill).
For claim 5, claim 3 is incorporated but the combination of Dahan in view of Kim fails to disclose the limitation of this claim, for which the reference of Petill is now introduced to teach as the method, further comprising:
determining based on the first dataset and the first user input, whether the collection of at least one second user input is required (Petill: [0055] — after a user provides input, the system attempts to disambiguate the user’s request and intention by applying the first dataset which includes information such as gaze direction and pointing or tapping gesture, to decide on if the system needs to a second user input).
The combination of Dahan in view of Kim provides teaching for obtaining a portion of a first dataset as current state information collected by the chatbot, but differs from the claimed invention in that the claimed invention further provides teaching for determining whether the collection of at least one second user input is required based on the first dataset and the first user input. This isn’t new to the art as the reference of Petill is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the known teaching of Petill which determines if second user input is required based on the first dataset and the first user input, with the teaching of the combination of Dahan in view of Kim which just teaches of obtaining current state information, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of clarifying a situation from the user’s first input that raises an ambiguity. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 19, system claim 19 and method claim 5 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to method claim 5.
Claims 10 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Dahan (US 2018/0054524 A1) in view of Kim (US 2019/0355351 A1) as applied to claims 1 and 15, and further in view of Price et al. (US 2020/0279490 A1: hereafter — Price).
For claim 10, claim 1 is incorporated but the combination of Dahan in view of Kim fails to disclose the limitation of this claim, for which Price is now introduced to teach as the method, wherein the one or more sensors are virtual sensors which receive inputs from online sources (Price: [0018] — a computer system that receives a signal from a remote sensor (teaching of this as a virtual sensor that receives remotes signals, the remote signal interpreted as being from an online source) so that the system can query a database (to further generate a query)).
The combination of Dahan in view of Kim provides teaching for the presence of one or more sensors for collecting a second user input, but differs from the claimed invention in that the claimed invention further provides teaching for the presence of virtual sensors which receive input from online sources. This isn’t new to the art as the reference of Price is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the known teaching of Price which obtains further input from virtual sensors connected to online sources, with obtaining information from one or more sources as provided by the combination of Dahan in view of Kim, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of increasing the number of sources to pull from in order to properly generate the right information to clarify a first user input. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 24, system claim 24 and method claim 10 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 24 is similarly rejected under the same rationale as applied above with respect to method claim 10.
Claims 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Dahan (US 2018/0054524 A1) in view of Kim (US 2019/0355351 A1) as applied to claims 3 and 17, and further in view of Eakin et al. (US 11,211,058 B1: hereafter — Eakin).
For claim 11, claim 1 is incorporated but the combination of Dahan in view of Kim fails to disclose the limitation of this claim, for which Eakin is now introduced to teach as the method, further comprising:
applying a pre-determined threshold to determine whether it is required to collect the at least one second user input (Eakin: Col 39 lines 38–45 — checking natural language understanding for interpretations exceeding a threshold, and if they exceed a threshold, a disambiguation is triggered in order to determine the correct interpretation (the triggering of the disambiguation would elicit a response from the user as a second user input)).
The combination of Dahan in view of Kim provides teaching for collecting at least one second user input, but differs from the claimed invention in that the claimed invention further provides teaching for applying a pre-determined threshold to determine if collecting a second user input. This isn’t new to the art as the reference of Eakin is seen to teach above.
Hence, before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to combine the known teaching of Eakin which applies a threshold to determine if a second user input should be collected, with the collection of the one or more second user inputs as provided by the combination of Dahan in view of Kim, to thereby come up with the claimed invention. The combination of both prior art elements would have provided the predictable result of applying the presence of a level of confidence that the first user input was properly understood, the threshold serving the purpose of not requesting/collecting disambiguation input only if required by the system for obtaining clarification. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
As for claim 25, system claim 25 and method claim 11 are related as system and the method of using same, with each claimed element’s function corresponding to the claimed method step. Accordingly, claim 25 is similarly rejected under the same rationale as applied above with respect to method claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Telpaz et al. (US 2021/0064066 A1) provides teaching for a system that updates a decision model based on data obtained in response to user input [0014].
YU et al. (US 2017/0098197 A1) provides teaching for updating a decision-making process for future searches based on the obtained feedback from a user, wherein the feedback is received as information requested from a user [0043], the feedback being used to improve a decision-making process [0154], and the presence of a decision-making machine learning model [0138].
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to OLUWADAMILOLA M. OGUNBIYI whose telephone number is (571)272-4708. The Examiner can normally be reached Monday – Thursday (8:00 AM – 5:30 PM Eastern Standard Time).
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, PARAS D. SHAH can be reached at (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUWADAMILOLA M OGUNBIYI/Examiner, Art Unit 2653
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