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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/15/2026 has been entered.
DETAILED ACTION
The following Non-Final office action is in response to application 18/134475 filed on 1/15/2026.
Status of Claims
Claims 1-20 are currently pending and have been rejected as follows.
Response to Amendments
Double patenting rejection is held in abeyance. Rejections under 35 USC 101 are maintained and updated below. New rejections under 35 USC 103 are issued below.
Response to Arguments
Applicant’s 35 USC 101 arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 10-11 that the claims do not recite mental processes because humans cannot train linear regression models or automatically update digital object states Examiner respectfully disagrees. The MPEP states “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).”Further, the claims are considered abstract because these steps recite mathematical concepts such as mathematical calculations in addition to reciting mental processes. A claim can still be abstract because it recites mathematical concepts, even if not every step is a mental process.
Applicant argues on p. 11-13 that the claims integrate any abstract idea into a practical application because it solves the problem of information determining how a system would perform during a time interval being provided too late, thereby precluding a corrective action for improving performance during the time interval from being taken. Examiner respectfully disagrees. Getting transaction forecasts early enough to take action is not a technical problem inherent to computers, but rather is a business objective timing problem. The four limitation applicant points to do not demonstrate technological improvements consistent with MPEP 2106.05(a). The “updating” limitation merely states functional logic for handling activity/inactivity. The “extracting” limitation merely states an abstract step for improving the inputs to the forecast model. The “training” limitation merely applies abstract mathematical analysis. The “determining an aggregated value” limitation is itself a mathematical calculation.
Response to Arguments
Applicant’s prior art arguments and amendments have been fully considered but they are moot in light of the newly cited portions of the Hicks reference.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-10 of US Patent No. 11651237 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the instant application recite substantially the same claim language as the patent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, non-transitory computer readable medium, and system). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-7 are directed toward the statutory category of a process (reciting a “method”). Claims 8-14 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable medium”). Claims 15-20 are directed toward the statutory category of a machine (reciting a “system”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 8 and 15 are directed to an abstract idea by reciting for a tenant of a multi-tenant online system, storing a plurality of objects, wherein an object represents a potential transaction and is associated with a value of the potential transaction, the object having one of a plurality of object categories, the object configured to transition between states responsive to changes in data associated with the object; receiving interactions associated with the plurality of objects; updating states of the plurality of objects based on the received interactions, including changing active states to inactive in response to no interactions received within a predetermined time limit, and changing inactive states to active in response to receiving interactions relating to the plurality of objects; extracting features of a set of objects that were previously processed and interacted with by one or more users, the features including: a number of changes to a category of an object of the set since the object was created, a rate of category changes applied to an object of the set, and an age of an object of the set, wherein the age of the object indicates a time period since the object was created; training a plurality of linear regression models for a set of object categories, a sequence of sub-intervals in a time interval, and the tenant of the multi-tenant online system based on the extracted features, each of the plurality of linear regression models associated with one of the object categories in the set of categories, one of the sub-intervals in the sequence, and the tenant of the multi-tenant online system, wherein training each of the plurality of linear regression models comprises using a training dataset including extracted features associated with one of the object categories in the set of categories, one of the sub- intervals in the sequence, and the tenant of the multi-tenant online system; identifying a set of objects from the plurality of stored objects, the identified set of objects representing potential transactions that have been initiated but are yet to close; executing a subset of trained models from the plurality of trained models to determine total expected values of the identified set of objects for the one or more object categories; determining an aggregated value for an end of a time interval by aggregating the expected values across the plurality of object categories based on the identified set of objects; and sending the aggregated value for display … (Example Claim 1).
The claims are considered abstract because these steps recite mathematical concepts such as mathematical calculations and mental processes such as concepts performed in the human mind (including an observation, evaluation, judgement, opinion). The claims recite instructions to store data, extract features, train models, and use the models to predict and display a total expected value for a dataset. Applicant’s disclosure does not recite a particular problem the claimed steps aim to solve, however, it is understood that the claimed steps aim to provide recommendations to users in an enterprise based on the total value of potential transactions to focus on, to maximize the value to the enterprise (Applicant’s Specification, [0018]). By this evidence, the claims recite a type of mathematical concepts such as mathematical calculations and mental processes such as concepts performed in the human mind (including an observation, evaluation, judgement, opinion common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., a method, non-transitory computer readable medium, and system for predicting an aggregate value of objects representing potential transactions).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as computing device; and a computer-readable storage medium; a user interface) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Claims 5, 12, and 19 recite wherein historical data associated with the stored plurality objects is stored in a database table, the method further comprising: detecting a change in a value associated with an object representing a potential transaction; and adding a row to the database table responsive to detecting the change in the value. However, this amounts to updating a database and merely uses a computer as a tool to perform an abstract idea.
Dependent claims 2-7, 9-14, and 16-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP 2106.05(f).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea ‐ see MPEP
2106.05(f).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps of “storing” a plurality of objects, “receiving” interactions, “updating” states, “extracting” features, “training” a plurality of models, “identifying” a set of objects, “executing” a subset of trained models, “determining” an aggregated value, and “sending” the aggregated value for display (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-20 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 USC 103 as being unpatentable over the teachings of
Kahlow, US 20130204663 A1, cite no. 13 from IDS file 6/2/2023, hereinafter Kahlow. In view of
Hicks et al., US 20160063506 A1, hereinafter Hicks. As per,
Claims 1, 8, 15
Kahlow teaches
A method comprising: /
A non-transitory computer-readable storage medium comprising stored instructions that, when executed by a computing device, cause the computing device to perform operations including: /
A system comprising: computing device; and a computer-readable storage medium comprising stored instructions that, when executed by the computing device, cause the computing device to perform operations including: (Kahlow fig. 9; [0006])
for a tenant of a multi-tenant online system, storing a plurality of objects, wherein an object represents a potential transaction and is associated with a value of the potential transaction, the object having one of a plurality of object categories, the object configured to transition between states responsive to changes in data associated with the object; (Kahlow [0021] “The term "Indicator" or "visitor event" means a detected event or activity initiated by a visitor which relates to the client companies' products or services sold” corresponding to objects representing potential transactions; [0023] “System 10 collects multiple channels of visitor activity data and/or communication data, such as the types of data 11 listed on the left hand side in FIG. 1, relating to a particular client company or multiple client companies, and then scores visitors of the client company detected in the activity data, including both individuals and companies, in order to determine the following for each detected individual visitor and for each company visitor as a whole (taking into account all individual visitor activities found to be associated with a company): 1. The likelihood to purchase. 2. How far along in the sales cycle the prospective customer (company or individual) is. Are they in the early stage of buying or late stages? 3. What products/services/technologies (goods sold) companies are interested in buying. 4. Who they are--D&B company firmagraphic attributes (vertical, geography, size of business, industry, annual revenue, etc.)” corresponding to a tenant of a multi-tenant online system and the plurality of categorizations for the objects)
[…];
[…];
extracting features of a set of objects that were previously processed and interacted with by one or more users, […]; (Kahlow [0031] “Each category indicator and individual element level indicator is assigned a weighting … weighting is assigned based on linking stored visitor behavior to later sales data in the clients' CRM systems, making this system a more accurate predictor of potential future sales” corresponding to features of objects previously processed and interacted with one or more users)
training a plurality of linear regression models for a set of object categories based on the extracted features, each of the plurality of linear regression models associated with one of the object categories in the set of categories, one of the sub-intervals in the sequence, and the tenant of the multi-tenant online system, wherein training each of the linear regression plurality of models comprises using a training dataset including extracted features associated with one of the object categories in the set of categories, one of the sub- intervals in the sequence, and the tenant of the multi-tenant online system; (Kahlow [0029] “The data is first organized and dimensionalized in data organization module 25. Standard extract, transform and load (ETL) processes such as those provided in Adobe.RTM. Insight, Hadoop or others may be used to create dimensions out of the raw data. These dimensions are then sorted into action "Indicators" in event type weighting module 26. This is where the predictive model begins;” [0030] noting the category indicators; [0031] noting the assignment of weights; [0032] “Statistical methods may be used in order to determine the element level and category indicator weights for an initial sales predicting model for each new client company” corresponding to a plurality of models; [0040] “Other examples of methods for performing the weighting include machine learning statistical methods such as but not limited to decision trees, logistic regression, and linear regression” noting the linear regression models; [0050] “A similar methodology is used to determine what product category and what individual product an individual or company is interested in … indicators are only used if they map to a particular product or product category” noting the training done per tenant and product; [0052] and figs. 3-6 noting the X-axis representing the time interval by months and calculating monthly scores; [0071] “Process 800 may start with block 805 where data produced within a time period and associated with an entity (e.g., an individual or company) is identified” corresponding to the sub-interval)
[…];
executing a subset of trained models from the plurality of trained models to determine total expected values of the identified set of objects for the one or more object categories; (Kahlow [0044] “Target scoring can be done for visitors who are individuals and companies, to indicate the likelihood of purchasing any client company product or a specific product, service or technology” note the application of the trained model; [0060]-[0063] note the plurality of models built and updated)
determining an aggregated value for an end of a time interval by aggregating the expected values across the plurality of object categories based on the identified set of objects; and (Kahlow [0051] “The accumulated estimated sales prediction scores for companies and individuals showing interest in a client company over time are stored in database 22 and then converted into various reports and data extracts by report generating module 30 … The foregoing method is carried out periodically for each client company to provide them with sales prediction scores” note the periodic report corresponding to a time interval)
sending the aggregated value for display by a user interface. (Kahlow [0051] “Alerts may be sent in the event of a detected spike in scores for a particular visitor, product line, or product, for appropriate follow up by company personnel. The downstream use cases for sales and marketing are numerous;” [0057] “an activity spike or score spike indicates a potential new opportunity for the client company, and a decision engine detects such activity spikes and triggers an alert, which may include sending a personalized list of highly engaged contacts and companies to the appropriate next step sales contact, such as outbound telemarketing, sales assistant managers, and company partners” note the submission for display to multiple potential recipients)
Kahlow does not explicitly teach, Hicks however in the analogous art of data analysis teaches
receiving interactions associated with the plurality of objects; (Hicks [0006] “CRM systems store troves of data pertaining to the human-to-human interactions between representatives of an organization and corresponding customers” note the storing of interactions indicative of the receiving)
updating states of the plurality of objects based on the received interactions, including changing active states to inactive in response to no interactions received within a predetermined time limit, and changing inactive states to active in response to receiving interactions relating to the plurality of objects; (Hicks [0008] “the user interface to the CRM system is updated by removing from view any interactions having an elapsed time to a next stage that is greater than a threshold value;” [0018] “the interactions 120 occurring therebetween so as to cause a transition from one of the stages 110 to a next one of the stages 110” note the elapsed time greater than a threshold causing the CRM view to update; also note the interactions between stages causing a transition to a next stage)
[…] the features including: a number of changes to a category of an object of the set since the object was created, a rate of category changes applied to an object of the set, and an age of an object of the set, and state changes made to the set of objects during the durations of the objects, wherein the age of the object indicates a time period since the object was created; (Hicks fig. 1 noting Stages 1, 2, 3, etc. corresponding to the number of changes to a category; [0018] “FIG. 1 pictorially shows a process for the adaptation of a CRM user interface responsive to an analysis of CRM interactions data. As shown in FIG. 1, the sales cycle 100 for different customers can be recorded to include a set of stages 110 of each sales cycle 100 and the interactions 120 occurring therebetween so as to cause a transition from one of the stages 110 to a next one of the stages 110;” [0020] “The CRM user interface adaptation logic 180 further can determine the interactions associated with the contemporaneous stage 190A and can compute an elapsed time for each of the determined interactions until a next stage of the sales cycle is reached” corresponding to the rate of category changes; [0016] “each interaction between an organizational representative and a customer can be associated with a date of interaction … and an associated stage of the sales cycle” noting the creation date and current stage corresponding to the time period since the object was created)
identifying a set of objects from the plurality of stored objects, the identified set of objects representing potential transactions that have been initiated but are yet to close; (Hicks [0019] “The CRM user interface 140 can include a listing of selectable business opportunities 150 for different customers whether potential or existing, and a related stage of the sales cycle for each of the business opportunities” note the identification of a potential transaction for potential or existing customers and its related stage of the sales cycle; [0016] “the stage of the sales cycle can range from an initial or subsequent communication such as an e-mail message, to one or more interpersonal meetings, to the quotation of an offer for sale, the demonstration of a product or service, the closing of the sale” note the sales cycles ranging from initial communication to the closing of the sale)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Kahlow’s models to include receiving interactions, updating states, and changes to categories, a rate of change to categories, and an age of an object in view of Hicks in an effort to effectively identify the changes that resulted in the fastest advancement to a next stage in a sales cycle (see Hicks ¶ [0022] & MPEP 2143G).
Claims 2, 9, 16
Kahlow teaches
wherein at least one of the trained models is associated with a sub-interval of the time interval. (Kahlow [0009] “the collected data is organized into different event types and the collected event counts are separated between recent events and non-recent events” noting the time interval including recent and non-recent events; [0043] “the system may be programmed to double the value of an action/indicator count if the indicator was "recent" event, where "recent" means within a certain current recent time window. The recency flag can be set to any time window, for example within the last seven days, or a shorter or longer period” note the doubling of a recent value corresponding to a trained model associated with a sub-interval of the time interval)
Claims 3, 10, 17
Kahlow teaches
wherein the time interval includes the sequence of sub-intervals and the subset of trained models includes a trained model for each of the sub-intervals. (Kahlow fig. 3 noting the sequence of months corresponding to a sequence of sub-intervals; [0052] “The scores may be calculated at different time intervals depending on the type of business in which the company is involved. In the illustrated examples, the client companies for which contacts are being tracked are B2B companies with 6-12 month sales cycles, so the scores are calculated monthly” corresponding to a trained model for each of the sub-intervals)
Claims 4, 11, 18
Kahlow teaches
wherein each of the trained models in the subset of trained models includes is associated with (1) an object category of the set of object categories and (2) a sub-interval in the sequence of sub-intervals. (Kahlow Table 2 noting the multiple categories and the Non-Recent and Recent sub-intervals; [0048] “Table 2 below is an example of an individual cookies calculation only at the category level (for simplicity of explanation). The sales estimate processing system and method carries out the calculations of weighting and sales prediction scores at both the category and element level.”)
Claims 5, 12, 19
Kahlow teaches
wherein historical data associated with the stored plurality objects is stored in a database table, the method further comprising: detecting a change in a value associated with an object representing a potential transaction; and (Kahlow [0026] “The collected data files are stored in data storage module 22 and are also provided to data management module 24.”)
adding a row to the database table responsive to detecting the change in the value. (Kahlow [0026] “up to billions or more of rows of granular record level data and supplementary lookup attribute data (i.e. other data such as campaign and cost data) may be loaded;” [0041] “The predictive model may be periodically updated using new sales information and activities prior to sales;” [0049] “A total sales prediction score for a particular visitor (individual or company) can be tracked over time using the above calculation, with periodical updates of counts, as well as updates of weighting based on counts in categories or individual contact events” noting the updates to counts)
Claims 6, 13, 20
Kahlow teaches
wherein the set of objects that were previously processed includes objects representing previous potential transactions. (Kahlow [0008] “the system first uses collected data regarding visitor activity of one or more companies that have purchased from the client company in the past to calculate a sales predictive model”)
Claims 7, 14
Kahlow teaches
where the extracted features include at least one of: a rate of user interactions associated with the object within a past time interval; a rate of updates to the object; or total number of updates to the object since the potential transaction object was created. (Kahlow [0071] “Process 800 may start with block 805 where data produced within a time period and associated with an entity (e.g., an individual or company) is identified. The data may be collected from different sources or channels. Each collection or occurrence of an activity (e.g., a webpage visit, an email message, a text message, etc.) may be referred to as a "data page." At block 810, a channel-Q score or page-Q(p) score is computed for each of the channels of data. The channel-Q score may be an aggregate (e.g., an average) of the individual Q scores associated with the data pages of the channel” note the average of individual scores within a time period corresponding to a rate of user interactions associated with the object within a past time interval)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20160239919 A1; WO2008044227A2; Diaz-Aviles et al., Towards real-time customer experience prediction for telecommunication operators, 2015.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA MUNSON can be reached on (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624