DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 27 objected to because of the following informalities: “complising”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite the limitations “deriving corresponding message sequences” while the specification does not explain how this process is performed. The specification states “[0012] derived message sequences and with respect to responses received from the residents . . . deriving corresponding message sequences related to the derived payment behaviors . . . deriving AI-based decisions” and “[0026] communication is carried out using interactive conversational and/or generative AI (artificial intelligence).” MPEP 2161.01(l) and 2163.03(V) state:
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved
Although the specification and claims suggest the derivation of messages is based on generative AI, the specification does not disclose how this is performed algorithmically. Generative AI is a subset of artificial intelligence and relies on numerous algorithms capable of being implemented in multiple configurations. One of ordinary skill in the art is able to recognize that the use of AI is not sufficiently described.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 35 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are: there is no relationship between the limitations of the claim and the “interactive message generator” or any “training and implementing” of such an “interactive message generator”.
Claim 35 is a method claim; however, the limitations in the claim state the presence of a message generator and corresponding with residents implementing AI. The claim does not provide a structural element demonstrating that the message generator and implementing AI are connected. One of ordinary skill in the art is not able to determine whether the message generator uses AI to correspond with residents or if the correspondence with residents is independent from the implementation of AI.
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 21-40 are rejected under 35 U.S.C. 101 because
the claimed invention is directed to an abstract idea without significantly more. Claims 21, 28, and 35 recite(s) the series of steps instructing how to communicate with residents with respect to rent collection, which is a fundamental economic practice, a commercial or legal interaction, and the management of personal behavior or relationships or interactions between people and thus grouped as a certain method of organizing human activity. Claims 21 and 28 are similar methods with just the preamble of claim 21 being different. Claim 35 covers a different series of steps. This judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because storing and retrieving information in memory to train an artificial intelligence model in the broad context disclosed by the applicant is a well-understood, routine, conventional computer function as recognized by the court decisions listed in MPEP § 2106.05(d).
The analysis outlined in MPEP 2106 is used to determine subject matter eligibility. Regarding Eligibility Step 1, claims 21 and 35 state the subject matter is directed toward “an Al ( artificial intelligence) -based method of communicating with residents with respect to rent collection”. Claim 28 states the subject matter is directed to “a method of improving rent collection by a property management company and reducing evictions”, and “a method of using AI (artificial intelligence) to improve a rent collection system.” Claims 21, 28, and 35 cover material found in at least one of the four statutory categories for Eligibility Step 1 because they recite methods and therefore are interpreted as process claims.
Regarding Eligibility Step 2A, Prong One involves determining whether the claim recites an abstract idea, law of nature, or natural phenomenon. The preambles of claims 21 and 28 “method of communicating with residents with respect to rent collection” and “method of improving rent collection by a property management company and reducing evictions of residents managed”, respectively, are not given patentable weight based on the claim interpretation guidance in MPEP 2111.02 which states:
If the body of a claim fully and intrinsically sets forth all of the limitations of the claimed invention, and the preamble merely states, for example, the purpose or intended use of the invention, rather than any distinct definition of any of the claimed invention’s limitations, then the preamble is not considered a limitation and is of no significance to claim construction.
The bodies of claims 21 and 28 contain all the limitations of the claimed invention and their preambles only state intended use of the invention and do not provide any distinct definition of any of the invention’s limitations. After consideration, the preambles of claims 21 and 28 are not given patentable weight; claims 21 and 28 have the same limitations and are therefore analyzed in the same manner.
In the specification, “analyzing payment records” is disclosed in [0032] which states “analysis unit 120 configured to analyze payment records associated with the managed residents (from database 95) and derive payment behaviors 125 of the residents.” In the real estate industry, analyzing records and transactions to determine trends in payment activity is a commonplace business practice for mitigating risk.
In the specification, “ deriving payment behaviors of the residents, deriving corresponding message sequences related to the derived payment behaviors” is disclosed in [0078] which states “deriving payment behaviors of the residents (stage 220), deriving corresponding message sequences related to the derived payment behaviors, and with respect to a specified collection timeline ( e.g., including dates for due payments, grace periods, late fees and legal proceedings such filing for an eviction) . . . increase rent collection and decrease eviction rates by tailoring the correspondence to the deriving payment behaviors.” In the financial industry, determining and predicting the behaviors of consumers to provide them with targeted information such as promotional messages, advertisements, and payment plans is a commonplace practice for mitigating risk and facilitating consumer engagement.
In the specification, “sending messages and interactively corresponding with the residents to promote rent payments” is disclosed in [0043] which states “adjust messages 145 and/or correspondence 155 to specific types of residents, such as participants in a housing choice voucher program (Section8/HUD), rental assistance communities, students, etc., adjust the collection communication to receipt of payment by an assisting organization (e.g., municipality, government or other organizations), multiple payers per unit (e.g., several students), and/or to adjust messages 145 and/or correspondence 155 to specific types of properties, such as private or commercial properties (e.g., communication with a commercial entity is different from a private one), multi-family or single family properties.” In the real estate industry, leasing managers communicate interactively with residents to collect rent from tenants of commercial properties.
In the specification, “wherein at least one of the analyzing, the deriving and the sending is carried out at least partially by at least one computer processor” is disclosed in [0012] which states “wherein AI training is carried out at least partially by at least one computer processor.” This is extra-solution activity of using a processor to implement the method that has been described.
MPEP 2106.04(a)(2) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
These limitations cover the methods of organizing human activity. Analyzing payment behavior of residents is a fundamental economic principal and practice; and communicating with residents is directed to facilitate commercial and legal interactions, and business relations for obvious reasons.
Claim 22 states “wherein the specified collection timeline includes dates for due payments, grace periods, late fees and legal proceedings, and the specified collection rules include exceptions” which is disclosed by the specification in [00611] stating “collection timeline 70 may start on each month's 1st day, include 5 days of grace period and additional ten days of late fee period, before legal proceeding, debt collection and eviction notices and proceedings follow.” Rent collection timelines used to collect rent is a standard business practice in the real estate industry.
Claim 23 states “suggesting and following up the execution of payment plans for specific residents, and providing corresponding expected payment data” which is disclosed by the specification in [0054] stating “balance due and suggesting payment plans), thus encouraging quicker payment.” The presentation of payment plans and options to consumers is a standard economic practice.
Claim 24 states “adjusting contents and tone of the messages” and “escalating the tone and increasing a frequency of the messages” which is disclosed by the specification in [0068] stating “messages may include escalating tone or content, as well as options for alternative settling of the payment.” Adjusting the tone of messages sent to a consumer to facilitate a transaction is a standard business practice.
Claim 25 states “providing explanatory comments concerning a payment balance” which is disclosed by the specification in [0042] stating “provide explanatory comments concerning a payment balance of the specific resident, e.g., relate the balance to various types of fees (e.g., debt, missing installments, various payments, e.g., for water, pets, etc.) and provide explanations.” Providing explanations and comments regarding a transaction or upcoming transaction for documentation purposes (e.g. for a receipt) is standard business and economic practice.
Claim 26 states “displaying cyclic collection reports” and “alerts concerning non- payments” which is disclosed by the specification in [0085] stating “insights and scoring concerning the residents and alerting the property management company in cases of non-payment.” Displaying reports such as graphs and providing alerts to a consumer is a widespread economic practice.
Claim 27 states “reducing the total number of messages sent to residents by at least 5% and/or to increase collection of unpaid rent” which is disclosed by the specification in [0053] stating “reduce the total number of messages. . . with respect to rent collection carried out manually.” Reducing messages sent to residents and consumers to effectively communicate with them is a standard commercial practice to maximize customer engagement efficiently.
The limitations in the dependent claims for claims 21 and 28 cover the methods of organizing human activity. The limitations simply describe method involved in standard business practices such as customer engagement, reducing evictions, and facilitating transactions from tenants.
As it pertains to claim 35:
In the specification “correspond with residents in respect to rent collection by a property management company” is disclosed in [0037] which states “provide corresponding expected payment data.” It is standard practice to communicate with tenants in the real estate industry.
In the specification “engaging the residents according to predefined message sequences, derived with respect to payment behaviors by the individual residents” is disclosed in [0038] which states “good understanding of the current residents (behaviors, payment history, and even character) and also exhibit good judgment and decision making.” It is standard business practice to have a script of messages when communicating with consumers.
In the specification “corresponding with the residents implementing AI to promote rent collection according to the predefined message sequences and with respect to responses” is disclosed in [0083] which states “corresponding with the residents implementing AI to promote rent collection according to the predefined message sequences and with respect to responses received from the residents.” It is standard business practice to change the content of communications depending on information received from a consumer.
In the specification “derive insights and scoring concerning the residents and alerting the property management company in cases of non-payment” is disclosed in [0047] which states “derived payment behaviors of the residents 125 (e.g., lease information, parsed ledger data, payment history etc.) and enhanced by a resident scoring model.” Scoring and ranking consumers based on their past payment behavior is a standard financial practice.
In the specification “resolution of payment conflicts, to increase rent collection and reducing resident evictions” is disclosed in [0085] which states “optionally assisted by human guidance, concerning resolution of payment conflicts, to increase rent collection and reducing resident evictions.” It is a standard business practice to resolve payment conflicts. MPEP 2106.04(a)(2) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
These limitations cover the methods of organizing human activity. Analyzing payment behavior of residents is a fundamental economic principal and practice; and communicating with residents is directed to facilitate commercial and legal interactions, and business relations for obvious reasons.
Claim 36 states “deriving the predefined message sequences by continuous machine learning of payment behaviors by the residents, based on ledger data and on collected correspondence of the residents” which is disclosed by the specification in [0047] which states “correspondence 155 may be related to previously derived payment behaviors of the residents 125 (e.g., lease information, parsed ledger data, payment history etc.).” Deriving messages to send to consumers based on patterns in historical data collected in a ledger is a standard commercial practice.
Claim 37 states “adjusting the correspondence with the residents with respect to contents, tone and degree of required escalation” which is disclosed by the specification in [0040] which states “determining correspondence 155, e.g., the degree to which urgency is expressed, tone is escalated, frequency of messaging, timing of alerts.” Adjusting correspondence with consumers while communicating with them is a standard commercial practice.
Claim 38 states “escalating the tone and increasing a frequency of the messages upon non-payment and/or according to the specified collection rules” which is disclosed by the specification in [0040] which states “determining correspondence 155, e.g., the degree to which urgency is expressed, tone is escalated, frequency of messaging, timing of alerts.” Changing tone and frequency while communicating with a tenant based on their payment history is a commercial practice.
Claim 39 states “restricting the correspondence by specified proper language rules” which is disclosed by the specification in [0039] which states “adjust the communication language (e.g., based on prior information and/or based to detection of the language used in the communication), or switch languages.” Switching languages to communicate with consumers in their language is a beneficial and standard commercial practice.
Claim 40 states “displaying cyclic collection reports that provide data indicating a rent collection speed and remaining debt in the following month, as well as alert” which is disclosed by the specification in [0049] which states “cyclic collection reports that provide data indicating a rent collection speed and remaining debt in the following month, as well as alerts concerning nonpayments.” Displaying reports indicating transaction history is a commercial practice.
MPEP 2106.04(a)(2) states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
These limitations cover the methods of organizing human activity. Analyzing payment behavior of residents is a fundamental economic principal and practice; and communicating with residents is directed to facilitate commercial and legal interactions, and business relations for obvious reasons.
Regarding Eligibility Step 2A, Prong Two involves determining whether the claim recites additional elements that integrate the judicial exception into a practical application. The claims state “an Al ( artificial intelligence) -based” method. Artificial intelligence is the additional element cited in the claims. This additional element is extra-solution activity. MPEP 2106.05(g) states:
The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.
As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional.
iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93;
iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
[00411] states “Conversation and/or generative AI 150 may be configured to include negotiating payments and delays, addressing residents' general concerns and questions. Moreover, conversational and/or generative AI 150 may be configured to predict and/or understand if and when late payers are going to pay their balance, and adjust correspondence 155 accordingly.” It is well known that AI is capable of examining and reviewing historical financial records and holding conversations. The limitation of the method being based on AI further amounts to necessary data gathering and outputting. A leasing manager gathers payment data regarding tenants before requesting rent payments from tenants (e.g. checking whether a tenant has already made a rent payment or has made rent payments in the past). All uses of the recited judicial exception require such data gathering.
Regarding Eligibility Step 2B, the analysis involves determining whether the claim amounts to significantly more. The use of AI as it is presented in the claims generally links the use of the judicial exception to a particular technological environment. MPEP 2106.05 states:
Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself.
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
[0008] states “AI ( artificial intelligence) -based system” and “AI-based interactive message generator.” [0034] states “Interaction may be gradually improved by application of ML.” The applicant has not disclosed any technology for implementing AI but instead appears to be relying on existing AI technology to perform the claimed limitations involving AI based on the lack of disclosure of such AI technology. The claims suggest that AI has simply been appended to the process of analyzing financial records for the purpose of collecting rent and encouraging tenants to make timely payments.
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 21, 26-28, 33, 35-36, and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al.
Regarding claim 21, Hobbs teaches an Al ( artificial intelligence) -based method of communicating with residents with respect to rent collection ([0028] Use of artificial intelligence, and more specifically machine learning (a subset of artificial intelligence), facilitates the development of algorithms that engage tenants of rented housing units [0031] send a message . . . to the tenant informing them that their rent was not received on time [0010] The goal suggested to or selected by the renter can include paying rent on time in the future. [0012] paying rent on time [0013] asking rent for a residence),
the method comprising: analyzing payment records associated with managed residents and deriving payment behaviors (action to take based on the analysis) of the residents ([0007] receive at least one renter data input relating to renter behavior, analyze the at least one renter data input via processor, and determine at least one action to take based on the analysis [0015] analysis of renter behaviors in order to drive a desired outcome [0029] analyze the at least one renter data input via processor, and determine at least one action to take based on the analysis. [0036] The action taken by the renter resulting in the financial reward can include . . . paying rent on time [0069] Financial transaction information can be routed from banks, payment services and the like to the Stake organization to populate the Stake database with metrics to analyze renter behaviors.),
wherein the method is carried out to communicate with at least hundreds or thousands of residents simultaneously ([0102] it is to be understood that such features are not limited to serial execution, but rather, any number of threads, processes, services, servers, or the like that may execute asynchronously, concurrently, in parallel, simultaneously, synchronously, or the like are contemplated by the disclosure.),
and wherein at least one of the analyzing, the deriving and the sending is carried out at least partially by at least one computer processor ([0106] the system and process flows described herein represent various processes which may be substantially represented in computer-readable media and so executed by a computer or processor).
Hobbs does not explicitly teach deriving corresponding message sequences related to the derived payment behaviors, and with respect to a specified collection timeline and specified collection rules, and sending messages and interactively corresponding with the residents to promote rent payments - according to the derived message sequences and with respect to responses received from the residents, wherein the derived message sequences and the interactive messaging are specific to individual residents.
However, Ball in a similar field of endeavor discusses a digital assistant that presents message templates derived using machine learning. Ball teaches deriving corresponding message sequences related to the derived payment behaviors (transaction history), and with respect to a specified collection timeline (upcoming payment) and specified collection rules (spending limit) ([0129] generate messaging graphical user interfaces in response to certain determinations being made regarding the customer's finances . . . the customer spending above or below the customer spending limit, the customer having insufficient funds in the customer reserve account to make an upcoming payment, a detected change in the customer's income [0171] machine learning models may save information regarding each users (e.g., all users associated with the financial institution) that includes each user's transaction history . . . based on the time of year . . . digital assistants described above) may advice the user),
and sending messages and interactively corresponding with the residents to promote rent payments - according to the derived message sequences and with respect to responses received from the residents ([32] [78] [0129] a messaging applet configured to generate messaging . . . Each messaging template may have an associated set of responses that may be presented as options to respond to the messages presented to the customer. Additional message templates may be presented to the customer based on the customer's responses to the messages.),
wherein the derived message sequences and the interactive messaging are specific to individual residents ([0129] messaging applet configured to generate messaging graphical user interfaces in response to certain determinations being made regarding the customer's finances.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the processor that analyzes renter data input to determine an action to take based on the analysis in Hobbs per the generation of messaging templates in response to determinations made regarding a customer’s finances in Ball to allow the modified invention to offer customized guidance and personalized support to a tenant.
Regarding claim 26, Hobbs teaches generating and displaying cyclic collection reports that provide data indicating a rent collection speed and remaining debt in the following month, as well as alerts concerning non- payments ([0064] FIG. 2 presents a further GUI . . . indicating the amount of money that has been paid in the account . . . the scale of the GUI can correspond to a savings goal of the renter [0031] prepare and send a message (e.g., email, SMS text, or the like) to the tenant informing them that their rent was not received on time [Fig. 6 shows the number of days left to reach the goal indicating that the report is cyclic]).
Regarding claim 27, Hobbs teaches reducing the total number of messages sent to residents by at least 5% and/or to increase collection of unpaid rent by at least 30% - with respect to rent collection carried out manually ([0015] to reduce delinquency in rental payments).
Regarding claim 28, Hobbs discloses a method of improving rent collection by a property management company and reducing evictions of residents managed thereby ([0016] methods . . . for inducing renters to perform a desired task [0006] management company [0015] to reduce delinquency in rental payments),
the method comprising: analyzing payment records associated with managed residents and deriving payment behaviors (action to take based on the analysis) of the residents ([0007] receive at least one renter data input relating to renter behavior, analyze the at least one renter data input via processor, and determine at least one action to take based on the analysis [0015] analysis of renter behaviors in order to drive a desired outcome [0029] analyze the at least one renter data input via processor, and determine at least one action to take based on the analysis. [0036] The action taken by the renter resulting in the financial reward can include . . . paying rent on time [0069] Financial transaction information can be routed from banks, payment services and the like to the Stake organization to populate the Stake database with metrics to analyze renter behaviors.),
wherein rent collection is increased and eviction rate is decreased by tailoring the correspondence to the deriving payment behaviors of the residents and to the responses received from the residents ([0015] to reduce delinquency in rental payments [0031] to help the renter manage their finances to help avoid being late in future payments),
wherein at least one of the analyzing, the deriving and the sending is carried out at least partially by at least one computer processor ([0016] executed by the one or more computer processor),
and wherein the total number of messages sent to residents is reduced by at least 5% and/or collection of unpaid rent is increased by at least 30% - with respect to rent collection carried out manually ([0015] to reduce delinquency in rental payments).
Hobbs does not explicitly teach deriving corresponding message sequences related to the derived payment behaviors, and with respect to a specified collection timeline and specified collection rules, and sending messages and corresponding interactively with the residents to promote rent payments - according to the derived message sequences and with respect to responses received from the residents, wherein the derived message sequences and the interactive messaging are specific to individual residents.
However, Ball in a similar field of endeavor discusses a digital assistant that presents message templates derived using machine learning. Ball teaches deriving corresponding message sequences related to the derived payment behaviors (transaction history), and with respect to a specified collection timeline (upcoming payment) and specified collection rules (spending limit) ([0129] generate messaging graphical user interfaces in response to certain determinations being made regarding the customer's finances . . . the customer spending above or below the customer spending limit, the customer having insufficient funds in the customer reserve account to make an upcoming payment, a detected change in the customer's income [0171] machine learning models may save information regarding each users (e.g., all users associated with the financial institution) that includes each user's transaction history . . . based on the time of year . . . digital assistants described above) may advice the user),
and sending messages and corresponding interactively with the residents to promote rent payments - according to the derived message sequences and with respect to responses received from the residents ([0129] a messaging applet configured to generate messaging . . . Each messaging template may have an associated set of responses that may be presented as options to respond to the messages presented to the customer. Additional message templates may be presented to the customer based on the customer's responses to the messages.),
wherein the derived message sequences and the interactive messaging are specific to individual residents ([0129] messaging applet configured to generate messaging graphical user interfaces in response to certain determinations being made regarding the customer's finances.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations suggested to the tenants in Hobbs per Ball to allow the modified invention to encourage timely payments.
Regarding claim 35. Hobbs discloses a method of using AI (artificial intelligence) to improve a rent collection system ([0028] Use of artificial intelligence, and more specifically machine learning (a subset of artificial intelligence), facilitates the development of algorithms that engage tenants of rented housing units [0031] send a message . . . to the tenant informing them that their rent was not received on time [0010] The goal suggested to or selected by the renter can include paying rent on time in the future. [0012] paying rent on time [0013] asking rent for a residence),
the method comprising: training and implementing an interactive message generator to correspond with residents in respect to rent collection by a property management company ([0056] A server can track various parameters of the tenant over time, and make recommendations to a property manager or landlord based on machine learning.),
using AI to derive insights and scoring concerning the residents ([0036] The action taken by the renter . . . include . . . improving their credit score [0040] determine a relevance score of the renter).
and alerting the property management company in cases of non-payment, and deriving AI-based decisions, optionally assisted by human guidance, concerning resolution of payment conflicts, to increase rent collection and reducing resident evictions ([0030] providing a recommendation to a landlord identifying at least one action to take based on the analysis [0036] paying rent on time [0015] to reduce delinquency in rental payments [0059] interface . . . may be presented to the landlord and/or renter via the graphical user interface [optionally assisted by human guidance]),
wherein AI training is carried out at least partially by at least one computer processor ([0056] machine learning . . . one or more computer processor circuits).
Hobbs does not explicitly teach engaging the residents according to predefined message sequences, derived with respect to payment behaviors by the individual residents, and with respect to a specified collection timeline and specified collection rules, corresponding with the residents implementing AI to promote rent collection according to the predefined message sequences and with respect to responses received from the residents.
However, Ball in a similar field of endeavor discusses a digital assistant that presents message templates derived using machine learning. Ball teaches engaging the residents according to predefined message sequences, derived with respect to payment behaviors by the individual residents, and with respect to a specified collection timeline and specified collection rules ([0129] [0171]),
corresponding with the residents implementing AI to promote rent collection according to the predefined message sequences and with respect to responses received from the residents ([0129] [0171]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations suggested to the tenants in Hobbs per the messaging applet of Ball to allow the modified invention to encourage timely payments.
Regarding claim 36, Hobbs teaches the limitations of claim 35.
Hobbs does not explicitly teach further comprising deriving the predefined message sequences by continuous machine learning of payment behaviors by the residents, based on ledger data and on collected correspondence of the residents with the interactive message generator.
Ball teaches further comprising deriving the predefined message sequences by continuous machine learning of payment behaviors by the residents, based on ledger data (bills, amounts of bills) and on collected correspondence of the residents with the interactive message generator ([0129] [0171] transaction history, bills, amounts of bills, geographic information, user preferences, age, demographic, income).
Regarding claims 33 and 40, the limitations of claims 33 and 40 are rejected in the analysis of claim 26 above and these claims are rejected on that basis.
Claim(s) 22 and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al. and further in view of application US20020069230A1 by Schubert, JR. et al.
Regarding claim 22, Hobbs and Ball teach the limitations of claim 21.
The combined system of Hobbs and Ball does not explicitly teach dates for due payments, grace periods, late fees and legal proceedings, and the specified collection rules include exceptions to the specified collection timeline.
However, Schubert, JR., in a similar field of endeavor, discloses managing rent schedules and recurring payments in commercial real estate. Schubert, JR. further teaches wherein the specified collection timeline (rent schedules) includes dates for due payments (due date for such rent or other charge), grace periods (grace period), late fees (late fee) and legal proceedings (legal information), and the specified collection rules include exceptions (security deposits) to the specified collection timeline ([0086] [0142] [0030] [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs and Ball per the rent schedules of Schubert, JR. to allow the modified invention to alert property managers and renters of approaching payment deadlines.
Regarding claim 29, the limitations of claim 29 are rejected in the analysis of claim 22 above and this claim is rejected on that basis.
Claim(s) 23 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al. and further in view of application US20150120519A1 by Collins et al.
Regarding claim 23, Hobbs and Ball teach the limitations of claim 21.
The combined system of Hobbs and Ball does not explicitly teach suggesting and following up the execution of payment plans for specific residents, and providing corresponding expected payment data.
However, in a similar field of endeavor, Collins discloses the suggestion of payment strategies in commercial real estate.
Collins further teaches suggesting and following up the execution of payment plans for specific residents, and providing corresponding expected payment data ([0042] provide a suggested payment strategy indicating the suggested payment plan to the property owner. In another example, the property owner may implement the suggested payment plan by setting aside the amounts suggested until the due date arrives, at which time the set-aside funds can be used to pay [0043] generate an estimated . . . report 105 detailing some or all of this information [0064] FIG. 4 presents a further GUI for processing a bank transfer from the renter's Stake™ account to an outside financial institution).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs and Ball per the suggested payment strategy of Collins to allow the modified invention to collect early payments from a renter.
Regarding claim 30, the limitations of claim 30 are rejected in the analysis of claim 23 above and this claim is rejected on that basis.
Claim(s) 24, 31, and 37-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al. and in view of application US20230112921A1 by Cai et al. and further in view of application US20170126826A1 by Liu et al.
Regarding claim 24, Hobbs and Ball teach the limitations of claim 21.
The combined system of Hobbs and Ball does not explicitly teach adjusting contents and tone of the messages with respect to the analyzed payment records and with respect to the received responses, and further comprising escalating the tone and increasing a frequency of the messages upon non-payment and/or according to the specified collection rules.
However, in a similar field of endeavor Cai discloses an LLM that adjusts writing tone in response to a prompt received from a user and input training data and Liu in a similar field of endeavor discloses sending recurring notifications to a user when information is important.
Cai teaches further comprising adjusting contents and tone of the messages with respect to the analyzed payment records (input data) and with respect to the received responses (prompt), and further comprising escalating the tone ([0056] the LLM can both ideate suggestions and adjust the writing tone).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs and Ball per the adjustment in writing tone of Cai to allow the modified invention to prevent late payments.
Liu teaches increasing a frequency of the messages upon non-payment and/or according to the specified collection rules ([0044] repeated push notifications sent from the same smart device are received the second time to avoid missing significant information).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs, Ball, and Cai per the recurring significant notifications of Liu to allow the modified invention to prevent late payments.
Regarding claims 31, and 37-38; the limitations of claims 31, and 37-38 are rejected in the analysis of claim 24 above and these claims are rejected on that basis.
Claim(s) 25, 32, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al. and further in view of application US20220036210A1 by Sanghvi et al.
Regarding claim 25, Hobbs and Ball teach the limitations of claim 1.
The combined system of Hobbs and Ball does not explicitly teach providing explanatory comments concerning a payment balance of the specific resident, wherein the generative Al model is restricted by specified proper language rules.
However, Sanghvi in a similar field of endeavor discusses a natural language processing system that messages a user about account information.
Sanghvi teaches further comprising using a generative Al ( artificial intelligence) model for interacting with the residents by messages, providing explanatory comments concerning a payment balance of the specific resident, wherein the generative Al model is restricted by specified proper language rules ([0056] the AI system may proactively message the customer to provide updates about anticipated purchases, account activity, account balance . . . the messages may be sent via a natural language processing system . . . natural languages [0050] an intelligent chat bot may utilize natural language processing to converse with the user).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs and Ball per the natural language processing of Sanghvi to allow the modified invention to generate an invoice.
Regarding claims 32 and 39, the limitations of claims 32 and 39 are rejected in the analysis of claim 25 above and these claims are rejected on that basis.
Claim(s) 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over application US20230196484A1 by Hobbs et al. in view of application US20230377034A1 by Ball et al. and further in view of application US20230360121A1 by Bireley et al.
Regarding claim 34, Hobbs and Ball teach the limitations of claim 33.
The combined system of Hobbs and Ball does not explicitly teach receiving therefrom approvals and denials for resident-specific exceptions, and further interacting with the residents accordingly.
However, Bireley in a similar field of endeavor discusses users requesting changes to transactions that are either approved or rejected by a manager. Bireley teaches further comprising configuring a GUI (graphical user interface) to display the reports and the alerts to the property management company, receiving therefrom approvals and denials for resident-specific exceptions, and further interacting with the residents accordingly ([0076] changes to tagged transactions are reviewed and either approved or rejected by an authorized user (e.g., loan officer, admin, or manager) in response to a user input instruction to approve or reject the category change, such as shown in respective GUIs).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the recommendations of the combined system of Hobbs and Ball per the reviewing of transactions of Bireley to allow the modified invention to resolve payment conflicts between property managers and renters.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah Beamon whose telephone number is (571) 272-5443. The examiner can normally be reached on Mon-Fri from 9:00am to 4:00pm.
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/NOAH BEAMON/Examiner, Art Unit 2454
/DOUGLAS B BLAIR/Primary Examiner, Art Unit 2454
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