DETAILED ACTION
This communication is a Non-Final Rejection Office Action in response to the 4/25/2025 filling of Application 19/190,172. Claims 1-20 are now presented.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case Claims 1-10 are directed toward an apparatus for predicting a resource growth pattern. Claims 11-20 are directed toward a method for predicting a resource growth pattern. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – 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; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 recites the abstract idea of missions detection and analysis and elect an applicable compliance methodology from a regulatory corpus which falls into the abstract idea categories of certain methods of organizing human activity and mental processes. The elements of Claim 1 that represent the Abstract idea include:
classify the third datum to a label selected from a plurality of labels based on the prioritization value, wherein classifying further comprises:
generating a representation of the third datum in a first space having a first number of dimensions, wherein generating at least the representation comprises using a first machine-learning process; and
projecting the representation of the third datum to a second space having a second number of dimensions, wherein projecting at least the representation comprises using a second machine-learning process and results in a projected representation of a second number of dimensions;
As per step 2A prong 1 of the eligibility analysis, claim 1 recites the abstract idea. The instant claims are directed to predicting a resource growth pattern which falls into the abstract idea categories of methods of organizing human activity and mental processes.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
The instant claim recites mental processes including observation, evaluation, judgment, opinion. For example, the classifying, generating and projecting steps are drawn to observation and evaluation. As such, the claim recites at least one abstract idea which falls into the abstract idea categories of certain methods of organizing human activity and mental processes. The elements of Claim 1 that represent the Abstract idea include:
MPEP 2106.04(a)(2) II. 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).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the claim are directed to managing personal relationships and behavior which is a method of organizing human activity.
Further, the claim recites mental processes including observation, evaluation, judgment, opinion. For example, the classifying, generating and projecting steps are drawn to observation and evaluation. As such, the claim recites at least one abstract idea.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using 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 claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
An apparatus for predicting a resource growth pattern, the apparatus comprising:
at least a processor;
a memory connected to the processor, the memory containing instructions configuring the at least a processor to:
receive a first datum from a user device, wherein the first datum describes a first activity pattern of the user device;
receive a second datum from a client device, wherein the second datum describes a second activity pattern of the user device;
retrieve a third datum, wherein the third datum describes a prioritization value of the first activity pattern relative to the second activity pattern;
generate an interface data structure, wherein the interface data structure configures a remote display device to display the resource growth pattern including displaying the representation based on the user-input datum.
However, the processor and memory communicatively coupled to the one or more processors are recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Further MPEP 2105.05(g) explains that data gathering and data output can be considered pre-solution activity and post-solution activity. See MPEP 2106.05(g) that states:
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.
In the instant case, the receipt a first datum from a user device and a second datum from a client device is considered mere data gathering which is incidental to the primary process in a similar way that obtaining information about credit card transactions to be analyzed was incidental to the primary process explained above. Further, MPEP 2106.05 also states Examiner should evaluate whether the extra-solution limitation is well known. In this case, the broadly recited receipt of data from a user device or client device is well known. The MPEP also cites several examples of mere data gathering that have been found to be insignificant extra-solution activity including 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 (see OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93); and obtaining information about transactions using the Internet to verify credit card transactions (see CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011))
Similarly, the display of the results of an analysis recited in the instant claims is not meaningfully different from the post solution activity of 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. Both the instant case and the example cited in the MPEP merely output the result of an analysis in a manner that is not integrated into the claim as a whole.
When viewing the generic display and data gathering in combination with the generic computer does not add more than when viewing the elements individually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In step 2B, the examiner must be determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d). As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, the receipt data is recited broadly in the claims. MPEP 2106.05(d) states receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed generically (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). As such, the broadly claimed receipt of data is considered well-known and conventional as established by the MPEP and relevant case law.
Further, the Examiner takes official notice that the display device to display the resource growth pattern is merely a conventional display and not an improvement to the technology.
When viewing the conventional display and data gathering in combination with the generic computer does not add more than when viewing the elements individually. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Further, Claims 2-10 further limit the mental processes and methods of organizing human activity already rejected in the parent claim, but fail to remedy the deficiencies of the parent claim as they do not impose any additional elements that amount to significantly more than the abstract idea itself. Further, claims 2, 9 disclose the additional elements of displaying information. But as explained above the display of information is considered insignificant extra solution activity and well-known and conventional.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 1-10 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. The presentment of claim 1 otherwise styled as a method, computer program product or system, for example, would be subject to the same analysis. As such, claims 11-20 are also rejected.
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.
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) 1, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mohler US 2016/0321935 A1 in view of Faucher-Courcheane US 20220129988 A1.
As per Claim 1 Mohler teaches an apparatus for predicting a resource growth pattern, the apparatus comprising:
at least a processor; (see Mohler para. 24)
a memory connected to the processor, the memory containing instructions configuring the at least a processor to: (see Mohler para. 24)
receive a first datum from a user device, wherein the first datum describes a first activity pattern of the user device; Mohler para. 68 teaches in another example, a goal object 105 associated with the “family” channel can represent a desire to maintain and continue to cultivate a relationship with a spouse. Goal attributes associated with this can include goal inputs and goal data associated with time spent together, locations visited together (e.g., gathered via check-ins from social networking sites), quality of time spent together, etc. Goal inputs can also include importing data from the spouse's corresponding goal objects, or from social network sites or other sources of data associated with the spouse. Goal logic for this goal can include comparing the time spent and the number of “dates” together over a period of time with the statistics of time spent and dates of a sample size of couples, etc. and their relative states of their relationships as a function of the time spent, dates, and other quantifiable aspects of their relationship. Additionally, goal logic for this goal can include inferring, by the goal engine 101 via inference rules, a spouse's satisfaction level based on the data gathered via social networking, emails to the spouse, etc. Goal conditions associated with this example can include meeting subjective expectations set by the participant, and/or by their spouse related to a comparative state relative to a population and/or a sustained satisfaction level.
receive a second datum from a client device, wherein the second datum describes a second activity pattern of the user device; ; Mohler para. 69 teaches in a further example, a goal object 105 associated with a health channel can include goal inputs and goal data from biometric sensors (e.g., blood monitors, heart rate monitors, GPS monitors to track running, sleep monitors, stress monitors, etc.), caloric intake data, participant weight data, body-mass index data, respiratory data and other health-related data. Goal logic can include logic associated with determining a participant's condition based on the data received, including logic associated with a change in a physical condition. Suitable logic can include algorithms and calculations used to determine health and medical status and conditions (e.g., those recognized by authoritative or regulatory organizations). Goal conditions can include reaching a particular weight, reaching a particular cholesterol level, being able to hit exercise milestones (e.g., running 5 miles every other day while maintaining a target heart rate within a desired range, etc.).
retrieve a third datum, wherein the third datum describes a prioritization value of the first activity pattern relative to the second activity pattern; Mohler para. 72 teaches in embodiments, the goal engine 101 can be configured to recognize “life events” in a participant's life that can affect the participant's abilities to achieve goals. Based on the life event, the goal engine 101 can generate recommendations regarding adjusting priorities and/or overall goals. The life events thus act as a trigger to the goal engine 101 that a participant's life has likely been substantially altered or changed, and that a change in goals and/or priorities may be necessary to adjust. Further para. 138-141 teaches the inventive subject matter can further include a client group “Gamification” to improve employee working/life balance. Or, rollup of departmental data to identify acidic management within a client organization. Such information, possibly including data on sleep results as reported by a wearable device, can be compiled and compares against norms or performance.
a. Two departments within the same company have a number of employees who have volunteered to have their ability to get a restful night's sleep captured, in exchange for a slight discount on their insurance (just for volunteering) along with a discount on the device. The employee data is rolled up and the managers get a general (no individual data) snapshot on how rested their employees are. This leads into the “group level gamification” where a company encourages their employees to chase good habits with managers acting as general cheerleaders (again no individual data).
b. From a company perspective, analytics that one department's people sleep more poorly than the industry average for that type of department (again the entity having a broader perspective across clients) provide a tool to look for acidic management.
c. An entity having or providing analytics on an individual's ability to sleep, could tie that to medical records and outcomes of procedures and medicine given by providers. Among other powerful outcomes, we could sell data (again PII removed) back to pharmaceutical companies on the effects of their medicine of people with good/bad sleep habits, and we can also report on if their medicine had a harmful effect on sleep after it has been prescribed.
classify the third datum to a label selected from a plurality of labels based on the prioritization value, wherein classifying further comprises: Mohler para. 61 teaches goal priority can be a priority of the goal object 105 representative of the importance of the goal in the participant's life relative to other goals. The goal priority can be user-designated and user-modified, as discussed herein, such that participants can re-arrange the priority of their goal objects 105 to reflect the changing priorities in their lives. The goal priority can include one or more of a global priority (e.g., among all goal objects 105 for a participant), a channel priority (e.g., among all goal objects within that channel), a temporal priority (e.g., a priority adjusted according to the importance of a goal at a particular time and/or for a particular duration), and a chronological priority (e.g., associated with completing or addressing a particular goal first before another goal).
generating a representation of the third datum in a first space having a first number of dimensions, Mohler para. 96 teaches FIGS. 5-6 provide illustrative examples of a user-facing portal presented via participant interface 104 providing a participant's life score 106. FIGS. 5-6 also show alternative displays 501,601 showing a participant's goals represented by goal objects 105. In the example of FIG. 5, the goals are visualized according to the boxes, arranged according to their priority attributes. In the example of FIG. 5, the participant's highest-priority goal is to “discover dinosaur species”, and the rest of the goals moving downward represent a descending priority. Also shown in the example of FIG. 5 are links between goals, illustrated via the arrows. As shown in FIG. 5, the “discover dinosaur species” goal is linked to a philanthropic goal of “volunteering at a museum”. The link between these goals can represent that progress towards of the “discover dinosaur species” (e.g., attributes associated with research into possible dig sites, keeping current with digging techniques to maximize success, etc.) can also benefit the goal of volunteering at a museum because the participant will be more knowledgeable about these topics and, as such, the time spent volunteering has more value and is more meaningful to the philanthropic goal. Thus, the contributions of both linked goals to the life score is enhanced. Likewise, the “lose weight” goal is shown as linked to the “retire at 65” goal and the “legacy” goal, such that progress towards the “lose weight” goal also works towards the “retire at 65” goal (as illustrated in the example above) and the “legacy” goal. The example of FIG. 5 shows the top ranked goals across all channels. The goals can show the channel to which they are associated (as shown in the “Philanthropic: Volunteer at Museum” goal) or lack any such markings (as shown in the other goals).
Mohler does not teach wherein generating at least the representation comprises using a first machine-learning process; and
projecting the representation of the third datum to a second space having a second number of dimensions, wherein projecting at least the representation comprises using a second machine-learning process and results in a projected representation of a second number of dimensions; and However, Faucher-Courcheane para. 90 teaches in possible implementations, the degree of commitment associated with the financial goals is performed using a trained machine learning model. The degree of commitment corresponds to a predicted probability outputted by the trained machine learning model that a specific financial goal will be achieved. Historical income data and historical expense data is inputted to the trained machine learning model, and the prediction or importance to assign to a goal is determined based in the historical data. Preferably, trained machine learning models can be used to predict the probability that the client (or related entities) will achieve the goals set. Two clients with identical financial wealth data, monthly income and spending and socio-economic data may have very different propensities to achieve particular life goals. For one, the goals may be a vague wish, or the client may have little discipline to save money to achieve the goal. For the other client, the goal may be a first priority and he will adjust his spending to achieve the goal. Financial data relating to spending habits can be used to predict the likelihood of achieving specific life goals. For each life goal, the “degree of commitment to goal determination module” 146 can collect or access existing client financial data, personal information data, socio-economic data and behavioural data and whether the client achieved or did not achieve the goal. The collected data can be labelled accordingly, and an AI model can be trained with this training data to predict the likelihood that a client will achieve the same goal. According to a possible implementation, different machine learning models can be trained for different life goals. In the example of FIG. 2, three trained AI-model (18, 18′, 18″) are shown, each having been trained and being able to predict the likelihood that a given client will be able to take a sabbatical year, will be able to retire early, or will be able to buy a house, but of course, there can be as many model as possible life goals that can be created in the system 10.
generate an interface data structure, wherein the interface data structure configures a remote display device to display the resource growth pattern including displaying the representation based on the user-input datum. Faucher-Courcheane para. 99 teaches in possible implementations, as shown in FIGS. 5B and 5C, a value of the estate at death may be displayed as a single number, as indicated by box 450. The indicator 70 is also displayed to indicate how likely it is that the client will achieve his/her goals. The GUI can include a pull-down menu or a box to change an assumption or one of the goals and have the financial projections recalculated automatically and displayed on the screen, including the updated net estate value 450′ and updated indicator 70′. For example, a user may change the retirement age from 65 to 62 or simulate a job loss. Both Mohler and Faucher-Courcheane are directed to goal management. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Mohler to include wherein generating at least the representation comprises using a first machine-learning process; and
projecting the representation of the third datum to a second space having a second number of dimensions, wherein projecting at least the representation comprises using a second machine-learning process and results in a projected representation of a second number of dimensions; and generate an interface data structure, wherein the interface data structure configures a remote display device to display the resource growth pattern including displaying the representation based on the user-input datum as taught by Faucher-Courcheane to generate indicators that provide a better overview of the likelihood that an individual will achieve his goals (see para. 6),
Claim 11 recites similar limitation to claim 1 and is rejected for similar reasons. Further, Mohler teaches a method for predicting a resource growth pattern, (see para.7)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEIRDRE D HATCHER whose telephone number is (571)270-5321. The examiner can normally be reached Monday-Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625
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